Comparative Analysis of Various Evolutionary Techniques of Load Balancing: A Review
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paper_title: Load sharing in distributed real-time systems with state-change broadcasts

paper_content:

A decentralized, dynamic load sharing (LS) method based on state-change broadcasts is proposed for a distributed real-time system. Whenever the state of a node changes from underloaded to fully loaded and vice versa, the node broadcasts this change to a set of nodes, called a buddy set, in the system. The performance of the method is evaluated with both analytic modeling and simulation. It is modeled first by an embedded Markov chain for which numerical solutions are derived. The model solutions are then used to calculate the distribution of queue lengths at the nodes and the probability of meeting tasks deadlines. The analytical results show that buddy sets of ten nodes outperform those of less than ten nodes, and the incremental benefit gained from increasing the buddy set size beyond 15 nodes is insignificant. These and other analytical results are verified by simulation. The proposed LS method is shown to meet task deadlines with a very high probability. >
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paper_title: Adaptive load sharing in homogeneous distributed systems

paper_content:

Rather than proposing a specific load sharing policy for implementation, the authors address the more fundamental question of the appropriate level of complexity for load sharing policies. It is shown that extremely simple adaptive load sharing policies, which collect very small amounts of system state information and which use this information in very simple ways, yield dramatic performance improvements. These policies in fact yield performance close to that expected from more complex policies whose viability is questionable. It is concluded that simple policies offer the greatest promise in practice, because of their combination of nearly optimal performance and inherent stability.
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paper_title: Subcube allocation and task migration in hypercube multiprocessors

paper_content:

Two important issues in managing processors in a hypercube are addressed: subcube allocation and task migration to eliminate the system fragmentation caused by allocation and deallocation of subcubes. It is proven that the subcube allocation strategy using a binary reflected Gray code (BRGC), called the GC strategy, possesses the best subcube recognition ability among all strategies using sequential searches. A binary code (BC) is defined as the binary representation of a nonnegative integer, and an extended binary code (EBC) is obtained by permutating the bits of a BC. Similarly, an extended Gray code (EGC) is obtained from a BRGC. The subcube recognition ability of an allocation strategy using multiple EBCs is analyzed and compared to that using multiple EGCs. >
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paper_title: Genetic Algorithms Data Structures Evolution Programs

paper_content:

Thank you for reading genetic algorithms data structures evolution programs. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this genetic algorithms data structures evolution programs, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some harmful virus inside their laptop.
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paper_title: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm

paper_content:

A natural river often finds good paths among lots of possible paths in its ways from the source to destination. These near optimal or optimal paths are obtained by the actions and reactions that occur among the water drops and the water drops with the riverbeds. The intelligent water drops (IWD) algorithm is a new swarm-based optimisation algorithm inspired from observing natural water drops that flow in rivers. In this paper, the IWD algorithm is tested to find solutions of the n-queen puzzle with a simple local heuristic. The travelling salesman problem (TSP) is also solved with a modified IWD algorithm. Moreover, the IWD algorithm is tested with some more multiple knapsack problems (MKP) in which near-optimal or optimal solutions are obtained.
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paper_title: An Intelligent Water Drop Algorithm for Solving Economic Load Dispatch Problem

paper_content:

Economic Load Dispatch (ELD) is a method of determining the most efficient, low-cost and reliable operation of a power system by dispatching available electricity generation resources to supply load on the system. The primary objective of economic dispatch is to minimize total cost of generation while honoring operational constraints of available generation resources. In this paper an intelligent water drop (IWD) algorithm has been proposed to solve ELD problem with an objective of minimizing the total cost of generation. Intelligent water drop algorithm is a swarm-based natureinspired optimization algorithm, which has been inspired from natural rivers. A natural river often finds good paths among lots of possible paths in its ways from source to destination and finally find almost optimal path to their destination. These ideas are embedded into the proposed algorithm for solving economic load dispatch problem. The main advantage of the proposed technique is easy is implement and capable of finding feasible near global optimal solution with less computational effort. In order to illustrate the effectiveness of the proposed method, it has been tested on 6-unit and 20-unit test systems with incremental fuel cost functions taking into account the valve point-point loading effects. Numerical results shows that the proposed method has good convergence property and better in quality of solution than other algorithms reported in recent literature. Keywords—Economic load dispatch, Transmission loss, Optimization, Valve point loading, Intelligent Water Drop Algorithm.
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paper_title: Particle swarm optimization

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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.
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paper_title: Particle Swarm Optimization: Performance Tuning and Empirical Analysis

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This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy functions and real life applications in engineering fields. The effectiveness of the algorithms is discussed.
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paper_title: Dynamic Task Scheduling with Load Balancing using Hybrid Particle Swarm Optimization

paper_content:

This paper presents a Hybrid Particle Swarm Optimization (HPSO) method for solving the Task Assignment Problem (TAP) which is an np-hard problem. Particle Swarm Optimization (PSO) is a recently developed population based heuristic optimization technique. The algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed setup. Load balancing which is a major issue in task scheduling is also considered. The nature of the tasks are independent and non pre-emptive. The HPSO yields a better result than the Normal PSO when applied to the task assignment problem. The results Of PSO and HPSO is also compared with another popular heuristic optimization technique namely Genetic Algorithm ( GA). The results infer that the PSO performs better than the GA.
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paper_title: A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems

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In a distributed system, a number of application tasks may need to be assigned to different processors such that the system cost is minimized and the constraints with limited resource are satisfied. Most of the existing formulations for this problem have been found to be NP-complete, and thus finding the exact solutions is computationally intractable for large-scaled problems. This paper presents a hybrid particle swarm optimization algorithm for finding the near optimal task assignment with reasonable time. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also, our method converges at a fast rate and is suited to large-scaled task assignment problems.
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paper_title: Max-Min Particle Swarm Optimization Algorithm with Load Balancing for Distributed Task Scheduling on the Grid Environment

paper_content:

In this paper proposes an algorithm namely max min Particle Swarm Optimization with load balancing techniques with the comparison of Swarm Intelligence Algorithms like Ant Colony Optimization. This algorithm is based on the task scheduling in grid environment. First of all we are calculating the QoS constraints to the PSO and ACO like, makespan, Cost and Deadline. Finally we had balanced the load to the particle swarm optimization, and compared with all other algorithms. We conclude the proposed algorithm is the best algorithm which is compared with ACO, PSO, Max PSO and Min PSO.
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paper_title: A RANDOMIZED LOAD BALANCING ALGORITHM IN GRID USING MAX MIN PSO ALGORITHM

paper_content:

Grid computing is a new paradigm for next generation computing, it enables the sharing and selection of geographically distributed heterogeneous resources for solving large scale problems in science and engineering. Grid computing does require special software that is unique to the computing project for which the grid is being used. In this paper the proposed algorithm namely dynamic load balancing algorithm is created for job scheduling in Grid computing. Particle Swarm Intelligence (PSO) is the latest evolutionary optimization techniques in Swarm Intelligence. It has the better performance of global searching and has been successfully applied to many areas. The performance measure used for scheduling is done by Quality of service (QoS) such as makespan, cost and deadline. Max PSO and Min PSO algorithm has been partially integrated with PSO and finally load on the resources has been balanced..
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Title: Comparative Analysis of Various Evolutionary Techniques of Load Balancing: A Review

Section 1: INTRODUCTION
Description 1: This section introduces the concept of load balancing, its importance, types of decisions involved (static or dynamic), and traditional versus evolutionary techniques.

Section 2: REVIEW OF LITERATURE
Description 2: This section presents a review of significant work done on load balancing techniques by various researchers and scientists, discussing different approaches in detail.

Section 3: Genetic Algorithm
Description 3: This section covers the Genetic Algorithm (GA), its principles, mechanisms, advantages, and historical context.

Section 4: Ant Colony Optimization
Description 4: This section explains the Ant Colony Optimization (ACO) technique, its inspiration from natural ant behavior, and its application in optimization problems.

Section 5: Diffusion Algorithm
Description 5: This section discusses the Diffusion Algorithm (DA), its methodology of load balancing among nodes, and its advantages in various load situations.

Section 6: Tabu Search
Description 6: This section describes the Tabu Search (TS) technique, its adaptive memory and exploration features, and its application to optimization problems.

Section 7: Artificial Bee Colony
Description 7: This section introduces the Artificial Bee Colony (ABC) technique, inspired by the foraging behavior of honey bees, and explains its phases and advantages.

Section 8: Intelligent Water Drop
Description 8: This section details the Intelligent Water Drop (IWD) technique, its natural inspiration, properties, and mechanism for finding optimal paths.

Section 9: Particle Swarm Optimization
Description 9: This section explores the Particle Swarm Optimization (PSO), its swarm intelligence basis, iterative process, and communication structure among particles.

Section 10: Hybrid Particle Swarm Optimization
Description 10: This section discusses the Hybrid Particle Swarm Optimization (HPSO), its combination of PSO with other techniques, and the improved results from hybridization.

Section 11: Max-Min Particle Swarm Optimization
Description 11: This section covers the Max-Min Particle Swarm Optimization (Max-Min PSO) technique, focusing on its application in grid scheduling and resource allocation.

Section 12: COMPARATIVE ANALYSIS OF VARIOUS EVOLUTIONARY TECHNIQUES
Description 12: This section compares the different evolutionary techniques discussed, summarizing their advantages, limitations, and issues with reference to each other. 
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