An Effective Chaotic Cultural-Based Particle Swarm Optimization for Constrained Engineering Design Problems

In this paper, a novel chaotic cultural-based particle swarm optimization algorithm (CCPSO) is proposed for constrained optimization problems by employing cultural-based particle swarm optimization (CPSO) algorithm and the notion of chaotic local search strategy. In the CCPSO, the shortcoming of cultural-based particle swarm optimization (CPSO) that it is easy to trap into local minimum be overcome, the chaotic local search strategy is introduced in the influence functions of cultural algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method.

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Improved quantum-behaved particle swarm optimization with local search strategy

Journal of Algorithms & Computational Technology ◽

10.1177/1748301816654020 ◽

2016 ◽

Vol 11 (1) ◽

pp. 3-12 ◽

Cited By ~ 3

Author(s):

Maolong Xi ◽

Xiaojun Wu ◽

Xinyi Sheng ◽

Jun Sun ◽

Wenbo Xu

Keyword(s):

Particle Swarm Optimization ◽

Local Search ◽

Search Strategy ◽

Optimization Problems ◽

Particle Swarm ◽

Search Space ◽

Performance Comparison ◽

Swarm Optimization ◽

Fitness Value ◽

Local Search Strategy

Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

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A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽

10.3390/en14154613 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4613

Author(s):

Shah Fahad ◽

Shiyou Yang ◽

Rehan Ali Khan ◽

Shafiullah Khan ◽

Shoaib Ahmed Khan

Keyword(s):

Particle Swarm Optimization ◽

Optimization Problems ◽

Quantum Particle ◽

Particle Swarm ◽

Continuous Variables ◽

Design Problems ◽

Swarm Optimization ◽

Electromagnetic Design ◽

Quantum Particle Swarm Optimization ◽

Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

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