A Novel Particle Swarm Optimization for Constrained Engineering Optimization Problems

Jiao, Minghai; Tang, Jiafu

doi:10.1007/978-3-540-92137-0_9

Cited by 2 publications

(2 citation statements)

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“…In the recent past, the Gaussian distribution is also being used for generating acceleration coefficients and being used for solving constrained problems (CPSO-GD) (Krohling and Coelho, 2006). Jiao and Tang (2008) proposed constrained engineering optimisation via particle swarm optimisation (CEOPSO). The constraint handling mechanism in CEOPSO (Jiao and Tang, 2008) preserves some of the infeasible individuals in the population during search process.…”

Section: Introductionmentioning

confidence: 99%

“…Jiao and Tang (2008) proposed constrained engineering optimisation via particle swarm optimisation (CEOPSO). The constraint handling mechanism in CEOPSO (Jiao and Tang, 2008) preserves some of the infeasible individuals in the population during search process. The hybrid Nelder-Mead simplex method along with particle swarm optimisation method (NMPSO) (Zahara and Kao, 2009) is being reported as a better algorithm for solving COPs.…”

Section: Introductionmentioning

confidence: 99%

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Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems

Ali

Sabat

Udgata

2012

IJBIC

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Most of the real world science and engineering optimisation problems are non-linear and constrained. This paper presents a hybrid algorithm by integrating particle swarm optimisation with stochastic ranking for solving standard constrained numerical and engineering benchmark problems. Stochastic ranking technique that uses bubble sort mechanism for ranking the solutions and maintains a balance between the objective and the penalty function. The faster convergence of particle swarm optimisation and the ranking technique are the major motivations for hybridising these two concepts and to propose the stochastic ranking particle swarm optimisation (SRPSO) technique. In this paper, SRPSO is used to optimise 15 continuous constrained single objective benchmark functions and five well-studied engineering design problems. The performance of the proposed algorithm is evaluated based on the statistical parameters such mean, median, best, worst values and standard deviations. The SRPSO algorithm is compared with six recent algorithms for function optimisation. The simulation results indicate that the SRPSO algorithm performs much better while solving all the five standard engineering design problems where as it gives a competitive result for constrained numerical benchmark functions.

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Section: Introductionmentioning

confidence: 99%