SURVEY ON EVOLUTIONARY COMPUTATION TECH TECHNIQUES AND ITS APPLICATION IN DIFFERENT FIELDS
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paper_title: Swarm Intelligence and Image Segmentation

paper_content:

Image segmentation plays an essential role in the interpretation of various kinds of images. Image segmentation techniques can be grouped into several categories such as edge-based segmentation, region-oriented segmentation, histogram thresholding, and clustering algorithms (Gonzalez & Woods, 1992). The aim of a clustering algorithm is to aggregate data into groups such that the data in each group share similar features while the data clusters are being distinct from each other. The K-means algorithm is a widely used method used for finding the structure of data (Tou & Gonzalez 1974). This unsupervised clustering technique has a strong tendency to get stuck into local minima when finding an optimal solution. Therefore, clustering results are heavily dependent on the initial cluster centers distribution. Hence, the search for good initial parameters is a challenging issue and the clustering algorithms require a great deal of experimentation to determine the input parameters for the optimal or suboptimal clustering results. Competitive learning model introduced in (Rumelhart & Zipser, 1986) is an interesting and powerful learning algorithm which can be used in unsupervised training for image classification (Hung, 1993). Simple Competitive Learning (SCL), shows stability over different run trials but this stable result is not always the global optima. In fact, in some cases the SCL converges to local optima over all run trials and the learning rate needs to be adjusted in the course of experimentation so that the global optimization can be achieved. There are a number of techniques, developed for optimization, inspired by the behaviours of natural systems (Pham & Karaboga, 2000). Swarm intelligence (SI) including Ant Colony Optimization (ACO) introduced in (Dorigo et al., 1996) and Particle Swarm Optimization (PSO) introduced in (Kennedy & Eberhart, 1995) has been introduced in the literature as an optimization technique. There are several SI approaches for data clustering in the literature which use clustering techniques such as K-means algorithm. In most of these approaches ACO or PSO are used to obtain the initial cluster centers for the K-means algorithm. We propose a hybrid algorithm which combines SI with K-means. We also use the same method to combine SI with SCL. Our aim is to make segmentation results of both K-means and SCL less dependent on the initial cluster centers and learning rate respectively. Hence, their results are more accurate and stabilized by employing the ACO and PSO optimization techniques. This improvement is due to the larger search space provided by these techniques. In addition, our
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paper_title: A Taxonomy and Survey of Cloud Computing Systems

paper_content:

The computational world is becoming very large and complex. Cloud Computing has emerged as a popular computing model to support processing large volumetric data using clusters of commodity computers. According to J.Dean and S. Ghemawat [1], Google currently processes over 20 terabytes of raw web data. It's some fascinating, large-scale processing of data that makes your head spin and appreciate the years of distributed computing fine-tuning applied to today's large problems. The evolution of cloud computing can handle such massive data as per on demand service. Nowadays the computational world is opting for pay-for-use models and Hype and discussion aside, there remains no concrete definition of cloud computing. In this paper, we first develop a comprehensive taxonomy for describing cloud computing architecture. Then we use this taxonomy to survey several existing cloud computing services developed by various projects world-wide such as Google, force.com, Amazon. We use the taxonomy and survey results not only to identify similarities and differences of the architectural approaches of cloud computing, but also to identify areas requiring further research.
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paper_title: Process selection and tool assignment in automated cellular manufacturing using Genetic Algorithms

paper_content:

The purpose of this study is to develop an efficient heuristic for the process selection and part cell assignment problem. The study assumes a production environment where each part has several process plans, each manifested by a required set of tools. These tools can be assigned to different machines based on a tool-machine compatibility matrix. An additional assumption is that all relevant data such as periodic demand, processing time, processing cost, tool magazine capacity, tool changing time, tool life and tool cost are fixed and known. A mixed integer linear program which takes all relevant data into account is developed to minimize the production cost. The suggested solution approach to solve this model makes use of Genetic Algorithms: a class of heuristic search and optimization techniques that imitate the natural selection and evolutionary process. First, the encoding of the solutions into integer strings is presented, as well as the genetic operators used by the algorithm. Next, the efficiency and robustness of the solution procedure is demonstrated through several different examples.
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paper_title: Genetic algorithms: principles of natural selection applied to computation

paper_content:

A genetic algorithm is a form of evolution that occurs on a computer. Genetic algorithms are a search method that can be used for both solving problems and modeling evolutionary systems. With various mapping techniques and an appropriate measure of fitness, a genetic algorithm can be tailored to evolve a solution for many types of problems, including optimization of a function of determination of the proper order of a sequence. Mathematical analysis has begun to explain how genetic algorithms work and how best to use them. Recently, genetic algorithms have been used to model several natural evolutionary systems, including immune systems.
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paper_title: Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

paper_content:

Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants.
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paper_title: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment

paper_content:

The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
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paper_title: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment

paper_content:

The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
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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).
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paper_title: Swarm Intelligence and Image Segmentation

paper_content:

Image segmentation plays an essential role in the interpretation of various kinds of images. Image segmentation techniques can be grouped into several categories such as edge-based segmentation, region-oriented segmentation, histogram thresholding, and clustering algorithms (Gonzalez & Woods, 1992). The aim of a clustering algorithm is to aggregate data into groups such that the data in each group share similar features while the data clusters are being distinct from each other. The K-means algorithm is a widely used method used for finding the structure of data (Tou & Gonzalez 1974). This unsupervised clustering technique has a strong tendency to get stuck into local minima when finding an optimal solution. Therefore, clustering results are heavily dependent on the initial cluster centers distribution. Hence, the search for good initial parameters is a challenging issue and the clustering algorithms require a great deal of experimentation to determine the input parameters for the optimal or suboptimal clustering results. Competitive learning model introduced in (Rumelhart & Zipser, 1986) is an interesting and powerful learning algorithm which can be used in unsupervised training for image classification (Hung, 1993). Simple Competitive Learning (SCL), shows stability over different run trials but this stable result is not always the global optima. In fact, in some cases the SCL converges to local optima over all run trials and the learning rate needs to be adjusted in the course of experimentation so that the global optimization can be achieved. There are a number of techniques, developed for optimization, inspired by the behaviours of natural systems (Pham & Karaboga, 2000). Swarm intelligence (SI) including Ant Colony Optimization (ACO) introduced in (Dorigo et al., 1996) and Particle Swarm Optimization (PSO) introduced in (Kennedy & Eberhart, 1995) has been introduced in the literature as an optimization technique. There are several SI approaches for data clustering in the literature which use clustering techniques such as K-means algorithm. In most of these approaches ACO or PSO are used to obtain the initial cluster centers for the K-means algorithm. We propose a hybrid algorithm which combines SI with K-means. We also use the same method to combine SI with SCL. Our aim is to make segmentation results of both K-means and SCL less dependent on the initial cluster centers and learning rate respectively. Hence, their results are more accurate and stabilized by employing the ACO and PSO optimization techniques. This improvement is due to the larger search space provided by these techniques. In addition, our
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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.
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Title: SURVEY ON EVOLUTIONARY COMPUTATION TECHNIQUES AND ITS APPLICATION IN DIFFERENT FIELDS

Section 1: INTRODUCTION
Description 1: Provide an introduction to evolutionary computation, explaining its basis in biological evolution and its applications in solving computational problems.

Section 2: EVOLUTIONARY ALGORITHM
Description 2: Describe the concept of evolutionary algorithms, their steps, and various types such as Gene Expressing Programming, Genetic Algorithm, Genetic Programming, Evolutionary Programming, Evolution Strategy, Differential Evolution, and Differential Search Algorithm.

Section 3: IMAGE PROCESSING, CLOUD COMPUTING AND GRID COMPUTING
Description 3: Discuss the applications of evolutionary computation techniques in the fields of image processing, cloud computing, and grid computing, including specific algorithms used and their benefits.

Section 4: PARTICLE SWARM OPTIMIZATION (PSO)
Description 4: Explain the particle swarm optimization technique, its inspiration from flocking behavior, and its application areas.

Section 5: HONEYBEE ALGORITHM
Description 5: Describe the honeybee algorithm, its basis in the foraging behavior of honey bees, the steps involved in the algorithm, and its applications.

Section 6: CUCKOO SEARCH (CS)
Description 6: Discuss the cuckoo search algorithm, its inspiration from cuckoo birds' brood parasitism, and its rules and applications.

Section 7: CONTRIBUTION OF SWARM INTELLIGENCE ALGORITHM IN CLOUD COMPUTING, GRID COMPUTING AND IMAGE PROCESSING
Description 7: Explore how swarm intelligence algorithms contribute to solving problems in cloud computing, grid computing, and image processing, including specific examples and research findings.

Section 8: CONCLUSION
Description 8: Summarize the discussions, highlighting the effectiveness and widespread use of evolutionary computation techniques in various fields.