Journal ArticleDOI
Penalty function approach for the mixed discrete nonlinear problems by particle swarm optimization
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Through typical mathematical and structural optimization problems, the validity of the proposed approach for the MDNLP is examined and a useful method to determine the penalty parameter of penalty term for the discrete design variables is proposed.Abstract:
In this paper, the basic characteristics of particle swarm optimization (PSO) for the global search are discussed at first, and then the PSO for the mixed discrete nonlinear problems (MDNLP) is suggested. The penalty function approach to handle the discrete design variables is employed, in which the discrete design variables are handled as the continuous ones by penalizing at the intervals. As a result, a useful method to determine the penalty parameter of penalty term for the discrete design variables is proposed. Through typical mathematical and structural optimization problems, the validity of the proposed approach for the MDNLP is examined.read more
Citations
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Journal ArticleDOI
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
TL;DR: Particle swarm optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms as discussed by the authors.
Journal ArticleDOI
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
TL;DR: Particle swarm optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms as discussed by the authors.
Journal ArticleDOI
Particle swarm optimisation for discrete optimisation problems: a review
TL;DR: This paper analyses all strategies adopted in PSO for tackling discrete problems and discusses thoroughly about pros and cons of each strategy.
Journal ArticleDOI
A mixed-discrete Particle Swarm Optimization algorithm with explicit diversity-preservation
TL;DR: A modification of the Particle Swarm Optimization (PSO) algorithm is presented, which can adequately address system constraints while dealing with mixed-discrete variables, and is applied to a wide variety of standard test problems.
Journal ArticleDOI
An improved vector particle swarm optimization for constrained optimization problems
TL;DR: An improved vector particle swarm optimization (IVPSO) algorithm is proposed to solve COPs, based on the simple constraint-preserving method, and the performance of IVPSO is tested on 13 well-known benchmark functions.
References
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Proceedings ArticleDOI
A discrete binary version of the particle swarm algorithm
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
Book
Engineering Optimization : Theory and Practice
TL;DR: This chapter discusses Optimization Techniques, which are used in Linear Programming I and II, and Nonlinear Programming II, which is concerned with One-Dimensional Minimization.
Journal ArticleDOI
Recent approaches to global optimization problems through Particle Swarm Optimization
TL;DR: A Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.
Journal ArticleDOI
A particle swarm optimization for reactive power and voltage control considering voltage security assessment
TL;DR: In this article, a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA) is presented.
Journal ArticleDOI
An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design
B. K. Kannan,S. N. Kramer +1 more
TL;DR: An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one, and continuous variables is presented in this paper, where penalties are imposed on the constraints for integer/discrete violations.