Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems

Beheshti, Zahra; Shamsuddin, Siti Mariyam; Sulaiman, Sarina

doi:10.1155/2014/907386

Cited by 20 publications

(15 citation statements)

References 37 publications

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“…Further, in logistic dynamic particle optimization, an extensive simulation study was presented to discuss the effectiveness of the random topology and the design strategies of population topology. Beheshti et al [94] proposed an improved PSO scheme called fusion global-local-topology PSO (FGLT-PSO). The algorithm employed both global and local topologies in PSO to jump out of the local optima.…”

Section: Fpsomentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

685

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Fpsomentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

685

380

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“…1. Other researchers have already used these test functions for testing the performance of optimal algorithms [13,27,28]. All the problems have zero optimal solution.…”

Section: Resultsmentioning

confidence: 99%

“…For the verification of our suggested MPSO for solving the engineering problems, we will have to use it for solving an engineering design problem. The problem which we have chosen is the TEAM workshop problem 22 [27,28]. It is an optimization problem having 3 different parameters, about optimization of the configuration of a Superconducting Magnetic Energy Storage (SEMS) as shown in Fig.…”

Section: Applicationmentioning

confidence: 99%

Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

et al. 2018

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Particle swarm optimization is a stochastic optimal search algorithm inspired by observing schools of fishes and flocks of birds. It is prevalent due to its easy implementation and fast convergence. However, PSO has been known to succumb to local optima when dealing with complex and higher dimensional optimization problems. To handle the problem of premutature convergence in PSO, this paper presents a novel adaptive inertia weight strategy and modifies the velocity update equation with the new Sbest term. To maintain the diversity of the population a particular radius r is introduced to impulse cluster particles. To validate the effectiveness of the proposed algorithm, various test functions and typical engineering applications are employed, and the experimental results show that with the changing of the proposed parameter the performance of PSO improves when dealing with these complex and high dimensional problems.

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“…This motivates us to hybridize these exploitative mutation strategies to enhance the exploitation capability of BSA. In addition, this paper is also in light of some studies which have shown that it is an effective way to combine other optimization methods to improve the performance for real-world optimization problems [24][25][26][27].…”

Section: Motivationsmentioning

confidence: 99%

A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

Wang

Zhong

Yin

et al. 2015

Mathematical Problems in Engineering

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The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

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Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems

Cited by 20 publications

References 37 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

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