Evolutionary computation of unconstrained and constrained problems using a novel momentum-type particle swarm optimization

Liu, Jenn-Long; Lin, Jiann-Horng

doi:10.1080/03052150601131000

Cited by 44 publications

(64 citation statements)

References 14 publications

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“…This is a nonsmooth function and derivative-free methods should be applied when solving problem (9). Unlike the suggestions in [30] and [41], the penalty parameter in (10) will not be changing dynamically with the iteration number. To define an appropriate updating scheme for µ, one has to consider a safeguarded scheme to prevent the subproblems (9) from becoming ill-conditioned as the penalty parameter increases [7].…”

Section: The Penalty Function Methodsmentioning

confidence: 99%

“…Most of the time, the dynamic penalty term also relies on other parameters that depend on the problem at hand, and it is not an easy task to determine the best values for those parameters. Well succeeded applications of dynamic penalties within particle swarm optimization algorithms appear in [30,41]. Annealing penalties depend on a parameter known as temperature that approaches zero as iterations proceed.…”

Section: The Penalty Function Methodsmentioning

confidence: 99%

“…A variant of a dynamic nonstationary penalty function is herein used to solve constrained MPP [30,41]. In these papers, particle swarm optimization algorithms are implemented in conjunction with the penalty technique.…”

Section: The Penalty Function Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Multilocal Programming and Applications

Pereira

Ferreira

Pinho

et al. 2013

Handbook of Optimization

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Section: The Penalty Function Methodsmentioning

confidence: 99%

Section: The Penalty Function Methodsmentioning

confidence: 99%

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Multilocal Programming and Applications

Pereira

Ferreira

Pinho

et al. 2013

Handbook of Optimization

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“…), is a violation dependent constant: Y{Z) = 1 if Z < 0.1, and /(z) = 2, otherwise. See, for example, in [7,8]. We remark that only the inequahty constraints are used in the penahzed terms.…”

Section: Constraint-handling By a Penalty Techniquementioning

confidence: 99%

Constrained Multi-global Optimization using a Penalty Stretched Simulated Annealing Framework

Pereira¹,

Fernandes²

2009

AIP Conference Proceedings

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Abstract. This paper presents a new simulated annealing algorithm to solve constrained multi-global optimization problems. To compute all global solutions in a sequential manner, we combine the function stretching technique with the adaptive simulated annealing variant. Constraint-handling is carried out through a nondifferentiable penalty function. To benchmark our penalty stretched simulated annealing algorithm we solve a set of well-known problems. Our preliminary numerical results show that the algorithm is promising.

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“…The momentum-type PSO, proposed in previous work [17], has a delta momentum rule in the velocityupdating equation, and efficiently determines the physical motion of particles more reasonably than other PSO versions. The momentum-type PSO markedly outperformed the original PSO [9] and modified PSO [13] versions.…”

Section: Introductionmentioning

confidence: 99%

Novel Orthogonal Momentum‐Type Particle Swarm Optimization Applied to Solve Large Parameter Optimization Problems

Liu

Chang

2008

Journal of Artificial Evolution and Applications

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This study proposes an orthogonal momentum-type particle swarm optimization (PSO) that finds good solutions to global optimization problems using a delta momentum rule to update the flying velocity of particles and incorporating a fractional factorial design (FFD) via several factorial experiments to determine the best position of particles. The novel combination of the momentum-type PSO and FFD is termed as the momentum-type PSO with FFD herein. The momentum-type PSO modifies the velocity-updating equation of the original Kennedy and Eberhart PSO, and the FFD incorporates classical orthogonal arrays into a velocity-updating equation for analyzing the best factor associated with cognitive learning and social learning terms. Twelve widely used large parameter optimization problems were used to evaluate the performance of the proposed PSO with the original PSO, momentum-type PSO, and original PSO with FFD. Experimental results reveal that the proposed momentum-type PSO with an FFD algorithm efficiently solves large parameter optimization problems.

show abstract

Evolutionary computation of unconstrained and constrained problems using a novel momentum-type particle swarm optimization

Cited by 44 publications

References 14 publications

Multilocal Programming and Applications

Multilocal Programming and Applications

Constrained Multi-global Optimization using a Penalty Stretched Simulated Annealing Framework

Novel Orthogonal Momentum‐Type Particle Swarm Optimization Applied to Solve Large Parameter Optimization Problems

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