Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism

Authors:
Issam Mazhoud;Khaled Hadj-Hamou;Jean Bigeon;Patrice Joyeux
Affiliations:
Grenoble-INP/UJF-Grenoble 1/CNRS, G-SCOP UMR 5272, Grenoble F-38031, France;Grenoble-INP/UJF-Grenoble 1/CNRS, G-SCOP UMR 5272, Grenoble F-38031, France;Grenoble-INP/UJF-Grenoble 1/CNRS, G-SCOP UMR 5272, Grenoble F-38031, France;Grenoble-INP/UJF-Grenoble 1/CNRS, G-SCOP UMR 5272, Grenoble F-38031, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

Citing 12
Cited 1

Use of a self-adaptive penalty approach for engineering optimization problems

Computers in Industry
An analysis of particle swarm optimizers

An analysis of particle swarm optimizers
An effective co-evolutionary particle swarm optimization for constrained engineering design problems

Engineering Applications of Artificial Intelligence
Molecular docking with multi-objective Particle Swarm Optimization

Applied Soft Computing
Self-adaptive velocity particle swarm optimization for solving constrained optimization problems

Journal of Global Optimization
Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization

Applied Soft Computing
Differential evolution in constrained numerical optimization: An empirical study

Information Sciences: an International Journal
Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization

Journal of Global Optimization
Interval-based global optimization in engineering using model reformulation and constraint propagation

Engineering Applications of Artificial Intelligence
Unified particle swarm optimization for solving constrained engineering optimization problems

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A simple multimembered evolution strategy to solve constrained optimization problems

IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization

IEEE Transactions on Evolutionary Computation

Parameter optimization of PEMFC model with improved multi-strategy adaptive differential evolution

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses constrained and optimal engineering problems solved using an adapted particle swarm optimization (PSO) algorithm. In fact, a specific constraint-handling mechanism is presented. It consists of a closeness evaluation of the solutions to the feasible region. The total constraints violation is introduced as an objective function to minimize. Interval arithmetic is used to normalize the total violations. The resulting objective problem is solved using a simple lexicographic method. The new algorithm is called CVI-PSO for constraint violation with interval arithmetic PSO. The paper provides numerous experimental results based on a well-known benchmark and comparisons with previously reported results. Finally, a case study of the optimal design of an electrical actuator with several model reformulations is detailed.