A Survey on Evolutionary Computation: Methods and Their Applications in Engineering
---
paper_title: Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm

paper_content:

Cloud computing is an emerging technology and it allows users to pay as you need and has the high performance. Cloud computing is a heterogeneous system as well and it holds large amount of application data. In the process of scheduling some intensive data or computing an intensive application, it is acknowledged that optimizing the transferring and processing time is crucial to an application program. In this paper in order to minimize the cost of the processing we formulate a model for task scheduling and propose a particle swarm optimization (PSO) algorithm which is based on small position value rule. By virtue of comparing PSO algorithm with the PSO algorithm embedded in crossover and mutation and in the local research, the experiment results show the PSO algorithm not only converges faster but also runs faster than the other two algorithms in a large scale. The experiment results prove that the PSO algorithm is more suitable to cloud computing.
---
paper_title: Evolutionary computation: an overview

paper_content:

We present an overview of the most important representatives of algorithms gleaned from natural evolution, so-called evolutionary algorithms. Evolution strategies, evolutionary programming, and genetic algorithms are summarized, with special emphasis on the principle of strategy parameter self-adaptation utilized by the first two algorithms to learn their own strategy parameters such as mutation variances and covariances. Some experimental results are presented which demonstrate the working principle and robustness of the self-adaptation methods used in evolution strategies and evolutionary programming. General principles of evolutionary algorithms are discussed, and we identify certain properties of natural evolution which might help to improve the problem solving capabilities of evolutionary algorithms even further.
---
paper_title: Community Cloud Computing

paper_content:

Cloud Computing is rising fast, with its data centres growing at an unprecedented rate. However, this has come with concerns over privacy, efficiency at the expense of resilience, and environmental sustainability, because of the dependence on Cloud vendors such as Google, Amazon and Microsoft. Our response is an alternative model for the Cloud conceptualisation, providing a paradigm for Clouds in the community, utilising networked personal computers for liberation from the centralised vendor model. Community Cloud Computing (C3) offers an alternative architecture, created by combing the Cloud with paradigms from Grid Computing, principles from Digital Ecosystems, and sustainability from Green Computing, while remaining true to the original vision of the Internet. It is more technically challenging than Cloud Computing, having to deal with distributed computing issues, including heterogeneous nodes, varying quality of service, and additional security constraints. However, these are not insurmountable challenges, and with the need to retain control over our digital lives and the potential environmental consequences, it is a challenge we must pursue.
---
paper_title: A multilevel image thresholding using the honey bee mating optimization

paper_content:

Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper, a new multilevel MET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. This proposed method is called the maximum entropy based honey bee mating optimization thresholding (MEHBMOT) method. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu's method are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEHBMOT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other three thresholding methods, the segmentation results using the MEHBMOT algorithm is the best and its computation time is relatively low. Furthermore, the convergence of the MEHBMOT algorithm can rapidly achieve and the results validate that the proposed MEHBMOT algorithm is efficient.
---
paper_title: Efficient resource management for Cloud computing environments

paper_content:

The notion of Cloud computing has not only reshaped the field of distributed systems but also fundamentally changed how businesses utilize computing today. While Cloud computing provides many advanced features, it still has some shortcomings such as the relatively high operating cost for both public and private Clouds. The area of Green computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. In this paper a new framework is presented that provides efficient green enhancements within a scalable Cloud computing architecture. Using power-aware scheduling techniques, variable resource management, live migration, and a minimal virtual machine design, overall system efficiency will be vastly improved in a data center based Cloud with minimal performance overhead.
---
paper_title: Efficient Graph-Based Image Segmentation

paper_content:

This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.
---
paper_title: A Swarm Intelligence inspired algorithm for contour detection in images

paper_content:

Swarm Intelligence uses a set of agents which are able to move and gather local information in a search space and utilize communication, limited memory, and intelligence for problem solving. In this work, we present an agent-based algorithm which is specifically tailored to detect contours in images. Following a novel movement and communication scheme, the agents are able to position themselves distributed over the entire image to cover all important image positions. To generate global contours, the agents examine the local windowed image information, and based on a set of fitness functions and via communicating with each other, they establish connections. Instead of a centralized paradigm, the global solution is discovered by some principal rules each agent is following. The algorithm is independent of object models or training steps. In our evaluation we focus on boundary detection as a major step towards image segmentation. We therefore evaluate our algorithm using the Berkeley Segmentation Dataset (BSDS) and compare its performance to existing methods via the BSDS benchmark and Pratt's Figure of Merit.
---
paper_title: GENETIC ALGORITHM: REVIEW AND APPLICATION

paper_content:

Genetic algorithms are considered as a search process used in computing to find exact or a approximate solution for optimization and search problems. There are also termed as global search heuristics. These techniques are inspired by evolutionary biology such as inheritance mutation, selection and cross over. These algorithms provide a technique for program to automatically improve their parameters. This paper is an introduction of genetic algorithm approach including various applications and described the integration of genetic algorithm with object oriented programming approaches. The Genetic algorithm is an adaptive heuristic search method based on population genetics. Genetic algorithm were introduced by John Holland in the early 1970s (1).Genetic algorithm is a probabilistic search algorithm based on the mechanics of natural selection and natural genetics. Genetic algorithm is started with a set of solutions called population. A solution is represented by a chromosome. The population size is preserved throughout each generation. At each generation, fitness of each chromosome is evaluated, and then chromosomes for the next generation are probabilistically selected according to their fitness values. Some of the selected chromosomes randomly mate and produce offspring. When producing offspring, crossover and mutation randomly occurs. Because chromosomes with high fitness values have high probability of being selected, chromosomes of the new generation may have higher average fitness value than those of the old generation. The process of evolution is repeated until the end condition is satisfied. The solutions in genetic algorithms are called chromosomes or strings (2). In most cases, chromosomes are represented by lists or strings. Thus, many operations in genetic algorithm are operations on lists or strings. The very high level languages like Python or Perl are more productive in list processing or string processing than C/C++/Java. In bioinformatics, Python or Perl is widely used. A genetic algorithm is a search technique used in computing to find exact or approximate solutions to
---
paper_title: Natural Evolution Strategies

paper_content:

This paper presents natural evolution strategies (NES), a novel algorithm for performing real-valued dasiablack boxpsila function optimization: optimizing an unknown objective function where algorithm-selected function measurements constitute the only information accessible to the method. Natural evolution strategies search the fitness landscape using a multivariate normal distribution with a self-adapting mutation matrix to generate correlated mutations in promising regions. NES shares this property with covariance matrix adaption (CMA), an evolution strategy (ES) which has been shown to perform well on a variety of high-precision optimization tasks. The natural evolution strategies algorithm, however, is simpler, less ad-hoc and more principled. Self-adaptation of the mutation matrix is derived using a Monte Carlo estimate of the natural gradient towards better expected fitness. By following the natural gradient instead of the dasiavanillapsila gradient, we can ensure efficient update steps while preventing early convergence due to overly greedy updates, resulting in reduced sensitivity to local suboptima. We show NES has competitive performance with CMA on unimodal tasks, while outperforming it on several multimodal tasks that are rich in deceptive local optima.
---
paper_title: Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms

paper_content:

From the Publisher: ::: From the explosion of interest, research, and applications of evolutionary computation a new field emerges-evolutionary electronics. Focused on applying evolutionary computation concepts and techniques to the domain of electronics, many researchers now see it as holding the greatest potential for overcoming the drawbacks of conventional design techniques.Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms formally introduces and defines this area of research, presents its main challenges in electronic design, and explores emerging technologies. It describes the evolutionary computation paradigm and its primary algorithms, and explores topics of current interest, such as multi-objective optimization. The authors examine numerous evolutionary electronics applications, draw conclusions about those applications, and sketch the future of evolutionary computation and its applications in electronics. In coming years, the appearance of more and more advanced technologies will increase the complexity of optimization and synthesis problems, and evolutionary electronics will almost certainly become a key to solving those problems. Evolutionary Electronics is your key to discovering and unlocking the potential of this promising new field.
---
paper_title: GENETIC ALGORITHM: REVIEW AND APPLICATION

paper_content:

Genetic algorithms are considered as a search process used in computing to find exact or a approximate solution for optimization and search problems. There are also termed as global search heuristics. These techniques are inspired by evolutionary biology such as inheritance mutation, selection and cross over. These algorithms provide a technique for program to automatically improve their parameters. This paper is an introduction of genetic algorithm approach including various applications and described the integration of genetic algorithm with object oriented programming approaches. The Genetic algorithm is an adaptive heuristic search method based on population genetics. Genetic algorithm were introduced by John Holland in the early 1970s (1).Genetic algorithm is a probabilistic search algorithm based on the mechanics of natural selection and natural genetics. Genetic algorithm is started with a set of solutions called population. A solution is represented by a chromosome. The population size is preserved throughout each generation. At each generation, fitness of each chromosome is evaluated, and then chromosomes for the next generation are probabilistically selected according to their fitness values. Some of the selected chromosomes randomly mate and produce offspring. When producing offspring, crossover and mutation randomly occurs. Because chromosomes with high fitness values have high probability of being selected, chromosomes of the new generation may have higher average fitness value than those of the old generation. The process of evolution is repeated until the end condition is satisfied. The solutions in genetic algorithms are called chromosomes or strings (2). In most cases, chromosomes are represented by lists or strings. Thus, many operations in genetic algorithm are operations on lists or strings. The very high level languages like Python or Perl are more productive in list processing or string processing than C/C++/Java. In bioinformatics, Python or Perl is widely used. A genetic algorithm is a search technique used in computing to find exact or approximate solutions to
---
paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
---
paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
---
paper_title: Economic dispatch using particle swarm optimization: A review

paper_content:

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper.
---
paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
---
paper_title: Particle swarm optimization

paper_content:

Particle swarm optimization (PSO) has undergone many changes since its intro- duction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.
---
paper_title: Particle swarm optimization

paper_content:

Particle swarm optimization (PSO) has undergone many changes since its intro- duction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.
---
paper_title: Genetic simulated annealing algorithm for task scheduling based on cloud computing environment

paper_content:

Scheduling is a very important part of the cloud computing system. This paper introduces an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation. Algorithm considers the QOS requirements of different type tasks, the QOS parameters are dealt with dimensionless. The algorithm efficiently completes tasks scheduling in the cloud computing environment computing.
---
paper_title: Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization

paper_content:

The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. This paper proposes a cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm. The main contribution of our work is to balance the entire system load while trying to minimizing the make span of a given tasks set. The new scheduling strategy was simulated using the CloudSim toolkit package. Experiments results showed the proposed LBACO algorithm outperformed FCFS (First Come First Serve) and the basic ACO (Ant Colony Optimization).
---
paper_title: Honey bee behavior inspired load balancing of tasks in cloud computing environments

paper_content:

Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue.
---
paper_title: Swarm Intelligence Approaches for Grid Load Balancing

paper_content:

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.
---
paper_title: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding

paper_content:

Segmentation is a critical task in image processing. Bi-level segmentation involves dividing the whole image into partitions based on a threshold value, whereas multilevel segmentation involves multiple threshold values. A successful segmentation assigns proper threshold values to optimise a criterion such as entropy or between-class variance. High computational cost and inefficiency of an exhaustive search for the optimal thresholds leads to the use of global search heuristics to set the optimal thresholds. An emerging area in global heuristics is swarm-intelligence, which models the collective behaviour of the organisms. In this paper, two successful swarm-intelligence-based global optimisation algorithms, particle swarm optimisation (PSO) and artificial bee colony (ABC), have been employed to find the optimal multilevel thresholds. Kapur's entropy, one of the maximum entropy techniques, and between-class variance have been investigated as fitness functions. Experiments have been performed on test images using various numbers of thresholds. The results were assessed using statistical tools and suggest that Otsu's technique, PSO and ABC show equal performance when the number of thresholds is two, while the ABC algorithm performs better than PSO and Otsu's technique when the number of thresholds is greater than two. Experiments based on Kapur's entropy indicate that the ABC algorithm can be efficiently used in multilevel thresholding. Moreover, segmentation methods are required to have a minimum running time in addition to high performance. Therefore, the CPU times of ABC and PSO have been investigated to check their validity in real-time. The CPU time results show that the algorithms are scalable and that the running times of the algorithms seem to grow at a linear rate as the problem size increases.
---
paper_title: An artificial ant colonies approach to medical image segmentation

paper_content:

The success of image analysis depends heavily upon accurate image segmentation algorithms. This paper presents a novel segmentation algorithm based on artificial ant colonies (AC). Recent studies show that the self-organization of ants is similar to neurons in the human brain in many respects. Therefore, it has been used successfully for understanding biological systems. It is also widely used in many applications in robotics, computer graphics, etc. Considering the features of artificial ant colonies, we present an extended model for image segmentation. In our model, each ant can memorize a reference object, which will be refreshed when it finds a new target. A fuzzy connectedness measure is adopted to evaluate the similarity between target and the reference object. The behavior of an ant is affected by the neighbors and the cooperation between ants is performed by exchanging information through pheromone updating. Experimental results show that the new algorithm can preserve the detail of the object and is also insensitive to noise.
---
paper_title: Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation

paper_content:

Abstract K-means clustering is usually used in image segmentation due to its simplicity and rapidity. However, K-means is heavily dependent on the initial number of clusters and easily falls into local falls into local optimum. As a result, it is often difficult to obtain satisfactory visual effects. As an evolutionary computation technique, particle swarm optimization (PSO) has good global optimization capability. Combined with PSO, K-means clustering can enhance its global optimization capability. But PSO also has the shortcoming of easily falling into local optima. This study proposes a new image segmentation algorithm called dynamic particle swarm optimization and K-means clustering algorithm (DPSOK), which is based on dynamic particle swarm optimization (DPSO) and K-means clustering. The calculation methods of its inertia weight and learning factors have been improved to ensure DPSOK algorithm keeping an equilibrium optimization capability. Experimental results show that DPSOK algorithm can effectively improve the global search capability of K-means clustering. It has much better visual effect than K-means clustering in image segmentation. Compared with classic particle swarm optimization K-means clustering algorithm (PSOK), DPSOK algorithm has obvious superiority in improving image segmentation quality and efficiency.
---
paper_title: Effective Scheduling Algorithm for Load balancing (SALB) using Ant Colony Optimization in Cloud Computing

paper_content:

In today's environment, the day to day business operations of organizations heavily rely on the automated processes from enterprise IT infrastructures. The cloud computing is an internet based concept which are dynamically scalable in nature and provide the virtualized resources, which are provided as service over the internet. Load balancing is one of the main challenging issues in cloud computing which is require to distribute the dynamic workload across multiple nodes to ensure that no single node is overloaded. This paper implement SALB algorithm. The main contribution of our work is to balance the entire system load while trying to maximize and minimize the different parameter (Performance, SLA violation, minimum overhead, energy issue) . Our objective is to study existing ACO's and to develop an effective load balancing algorithm using ant colony optimization.
---
paper_title: Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm

paper_content:

Abstract In this paper, a comprehensive mathematical model is proposed for designing robust machine cells for dynamic part production. The proposed model incorporates machine cell configuration design problem bridged with the machines allocation problem, the dynamic production problem and the part routing problem. Multiple process plans for each part and alternatives process routes for each of those plans are considered. The design of robust cell configurations is based on the selected best part process route from user specified multiple process routes for each part type considering average product demand during the planning horizon. The dynamic part demand can be satisfied from internal production having limited capacity and/or through subcontracting part operation without affecting the machine cell configuration in successive period segments of the planning horizon. A genetic algorithm based heuristic is proposed to solve the model for minimization of the overall cost considering various manufacturing aspects such as production volume, multiple process route, machine capacity, material handling and subcontracting part operation.
---
paper_title: A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines

paper_content:

Abstract In this study, a robust optimization approach is developed for a new integrated mixed-integer linear programming (MILP) model to solve a dynamic cellular manufacturing system (DCMS) with unreliable machines and a production planning problem simultaneously. This model is incorporated with dynamic cell formation, inter-cell layout, machine reliability, operator assignment, alternative process routings and production planning concepts. To cope with the parts processing time uncertainty, a robust optimization approach immunized against even worst-case is adopted. In fact, this approach enables the system’s planner to assess different levels of uncertainty and conservation throughout planning horizon. This study minimizes the costs of machine breakdown and relocation, operator training and hiring, inter-intra cell part trip, and shortage and inventory. To verify the performance of the presented model and proposed approach, some numerical examples are solved in hypothetical limits using the CPLEX solver. The experimental results demonstrate the validity of the presented model and the performance of the developed approach in finding an optimal solution. Finally, the conclusion is presented.
---
paper_title: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization

paper_content:

A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter. We define a "no-hope" convergence criterion and a "rehope" method so that, from time to time, the swarm re-initializes its position, according to some gradient estimations of the objective function and to the previous re-initialization (it means it has a kind of very rudimentary memory). We then study two different cases, a quite "easy" one (the Alpine function) and a "difficult" one (the Banana function), but both just in dimension two. The process is improved by taking into account the swarm gravity center (the "queen") and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.
---
paper_title: A review of swarm robotics tasks

paper_content:

Swarm intelligence principles have been widely studied and applied to a number of different tasks where a group of autonomous robots is used to solve a problem with a distributed approach, i.e. without central coordination. A survey of such tasks is presented, illustrating various algorithms that have been used to tackle the challenges imposed by each task. Aggregation, flocking, foraging, object clustering and sorting, navigation, path formation, deployment, collaborative manipulation and task allocation problems are described in detail, and a high-level overview is provided for other swarm robotics tasks. For each of the main tasks, (1) swarm design methods are identified, (2) past works are divided in task-specific categories, and (3) mathematical models and performance metrics are described. Consistently with the swarm intelligence paradigm, the main focus is on studies characterized by distributed control, simplicity of individual robots and locality of sensing and communication. Distributed algorithms are shown to bring cooperation between agents, obtained in various forms and often without explicitly programming a cooperative behavior in the single robot controllers. Offline and online learning approaches are described, and some examples of past works utilizing these approaches are reviewed.
---
paper_title: Wind energy and green economy in Europe: Measuring policy-induced innovation using patent data

paper_content:

The green economy policy discourse has devoted a lot of attention to the design of public policy addressing low-carbon technologies. In this paper we examine the impacts of public R&D support and feed-in tariff schemes on innovation in the wind energy sector. The analysis is conducted using patent application data for four western European countries over the period 1977–2009. Different model specifications are tested, and the analysis highlights important policy interaction effects. The results indicate that both public R&D support and feed-in tariffs have positively affected patent application counts in the wind power sector. The (marginal) impact on patent applications of increases in feed-tariffs has also become more profound as the wind power technology has matured. There is also some evidence of policy interaction effects in that the impact of public R&D support to wind power is greater at the margin if it is accompanied by the use of feed-in tariff schemes. These results support the notion that technological innovation requires both R&D and learning-by-doing, and for this reason public R&D programs should typically not be designed in isolation from practical applications. The paper ends by outlining some important avenues for future research.
---
paper_title: On-Off control based particle swarm optimization for maximum power point tracking of wind turbine equipped by DFIG connected to the grid with energy storage

paper_content:

Abstract In this paper, particle swarm optimization (PSO) is proposed to generate an On-Off Controller. On-Off Control scheme based maximum power point tracking is proposed to control the rotor side converter of wind turbine equipped with doubly fed induction generator connected to the grid with battery storage. The Grid Side Converter (GSC) is controlled in such a way to guarantee a smooth DC voltage and ensure sinusoidal current in the grid side. Simulation results show that the wind turbine can operate at its optimum power point for variable speed and power quality can be improved.
---

Title: A Survey on Evolutionary Computation: Methods and Their Applications in Engineering

Section 1: Introduction
Description 1: This section introduces evolutionary computation (EC), discussing its principles, significance, and how it aligns with biological evolution to solve complex computational problems.

Section 2: Main Applications of EC
Description 2: This section explores the various applications of evolutionary computation, especially in the fields of image and signal processing and cloud computing (CC).

Section 3: General Steps in an EA
Description 3: This section outlines the general steps involved in evolutionary algorithms (EAs), detailing the iterative process to find optimized solutions.

Section 4: Differential Evolution (DE)
Description 4: This section describes the DE algorithm, its purpose in minimizing objective functions, and its ability to find global solutions efficiently.

Section 5: Differential Search Algorithm (DSA)
Description 5: This section explains the DSA, which uses the concept of migration and movement to solve real-valued numerical optimization problems.

Section 6: Genetic Programming (GP)
Description 6: This section discusses GP, focusing on its ability to optimize computer populations to perform specific user tasks.

Section 7: Evolutionary Programming (EP)
Description 7: This section elaborates on EP, highlighting its flexibility and unique characteristic of not following a constant pattern.

Section 8: Evolution Strategy (ES)
Description 8: This section overviews ES, describing its iterative process, mutation, and selection as search mechanisms.

Section 9: Genetic Algorithm (GA)
Description 9: This section covers GA, detailing its use of natural selection and heuristic search to solve problems like automatic electronic circuit generation.

Section 10: Gene Expressing Programming (GEP)
Description 10: This section explains GEP, which uses tree structures and genotypic information to adapt and solve complex problems.

Section 11: Swarm Intelligence Algorithms (SIAs)
Description 11: This section introduces swarm intelligence (SI) algorithms, with examples such as ant colony optimization, bees algorithm, cuckoo bird search, and particle swarm optimization.

Section 12: Ant Colony Optimization (ACO)
Description 12: This section describes ACO, a technique for finding optimal paths in graphs inspired by the behavior of ants.

Section 13: Bees Algorithm (BA)
Description 13: This section explores BA, inspired by the food foraging behavior of bees, used for combinatorial and continuous optimization.

Section 14: Cuckoo Bird Search Algorithm (CSA)
Description 14: This section details CSA, an optimization method inspired by the reproductive strategy of cuckoo birds.

Section 15: Particle Swarm Optimization (PSO)
Description 15: This section covers PSO, an iterative method inspired by the social behavior of birds and fish to find optimized solutions.

Section 16: Classic Real World Applications of EAs and SIAs
Description 16: This section outlines classic applications of EAs and SIAs, such as cloud computing scheduling and load balancing.

Section 17: CC Scheduling by GA
Description 17: This section discusses the use of GA in scheduling tasks in cloud computing environments for better load balancing.

Section 18: CC Scheduling by ACO and BA
Description 18: This section explores how combining ACO and BA can improve resource management and power consumption in cloud computing.

Section 19: Grid Load Balancing by ACO
Description 19: This section describes the application of ACO for uniform load distribution in grid computing.

Section 20: Image Processing by ACO and BA
Description 20: This section details the use of ACO and BA in image feature extraction, segmentation, and noise reduction.

Section 21: Newly Introduced Trends
Description 21: This section discusses newly emerging trends and improved methods in evolutionary computation.

Section 22: Improved Image Segmentation Via K-Means Clustering and PSO
Description 22: This section explains the improvements in image segmentation using dynamic PSO integrated with K-means clustering.

Section 23: Scheduling Algorithm for Load Balancing (SALB) in CC Based on ACO
Description 23: This section elaborates on SALB, an enhanced version of ACO for dynamic workload balancing in cloud computing.

Section 24: Improved Bi-Objective Dynamic Cell Formation Problem (DCFP) by Non-Dominated Sorting GA (NSGA-II)
Description 24: This section describes the NSGA-II hybrid meta-heuristic for solving dynamic cellular manufacturing system problems.

Section 25: Improvement of Robust Machine Cells for Dynamic Part Production (RMCDPP) by GA
Description 25: This section discusses a GA-based heuristic for cost reduction in industrial production systems.

Section 26: Integrated Mixed-Integer Linear Programming (MILP) Model to DCMS Problem with Uncertainty
Description 26: This section covers the use of MILP models for handling uncertain factors in dynamic cellular manufacturing systems.

Section 27: Robotic Applications
Description 27: This section explores the various applications of SIAs in robotics, focusing on tasks like object sorting, navigation, and task assignment.

Section 28: Power Control in Wind Turbines by GA and PSO
Description 28: This section describes the use of GA and PSO in designing wind farms and controlling power conversion for optimal energy generation.

Section 29: Conclusion
Description 29: This section summarizes the significance of EC, its applications in various fields, and the discussed algorithms for solving specific problems.