Discrete Particle Swarm Optimization with local search strategy for Rule Classification

For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO) is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.64 ◽

2010 ◽

Vol 20-23 ◽

pp. 64-69 ◽

Cited By ~ 2

Author(s):

Yong Quan Zhou ◽

Lingzi Liu

Keyword(s):

Particle Swarm Optimization ◽

Local Search ◽

Engineering Design ◽

Search Strategy ◽

Optimization Problems ◽

Particle Swarm ◽

Design Problems ◽

Swarm Optimization ◽

Engineering Design Problems ◽

Local Search Strategy

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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A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain

Journal of Industrial Engineering International ◽

10.1007/s40092-015-0126-6 ◽

2015 ◽

Vol 12 (1) ◽

pp. 29-43 ◽

Cited By ~ 1

Author(s):

Mehdi Seifbarghy ◽

Masoud Mirzaei Kalani ◽

Mojtaba Hemmati

Keyword(s):

Particle Swarm Optimization ◽

Supply Chain ◽

Local Search ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle ◽

Single Vendor

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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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