A Combined Genetic Algorithms-local Search Engine (GAs–LCE) in Constrained Nonlinear Programming
Faiz A. El-Qorashy, Hossam A. Nabwey, A. A. Mousa
Current Journal of Applied Science and Technology · pp. 324–333 · Published 10 Apr 2015
10.9734/BJAST/2015/17059Abstract
Evolutionary optimization provides robust and efficient techniques for solving complex real-world problems. The aim of this paper is to present an enhanced evolutionary algorithm for solving constraint nonlinear programming problems NLPPs, which based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Our proposed approach is made of two phases, firstly, phase I is a classical genetic algorithm, which based on the ideas of repair strategy and co-evolution. Secondly in phase II, Based on the k-means cluster algorithm, the search space is shrunk after phase I to the generated rectangular-atom with highly rate and concentrating the optimal solution region, so local search techniques will implemented in order to get more accurate optimal solution. Finally, the results of various experimental studies using a suite of benchmark functions have demonstrated the superiority of the proposed algorithm to finding the global optimal solution for constraint nonlinear programming problems.
References (14)
- 1 Genetic Algorithms in Search, Optimization and Machine Learning
- 2 Genetic Algorithms + Data Structures = Evolution Programs [DOI]
- 3 Nonlinear programming: theory and algorithms [DOI]
- 4 Applied Nonlinear Programming
- 5 Evolutionary Algorithms for Constrained Parameter Optimization Problems [DOI]
- 6 A Survey of Constraint Handling Techniques in Evolutionary Computation Methods [DOI]
- 7 Genocop III: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints [DOI]
- 8 A solution to the optimal power flow using genetic algorithm [DOI]
- 9 Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems [DOI]
- 10 Improved differential evolution algorithm for nonlinear programming and engineering design problems [DOI]
- 11 Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms [DOI]
- 12 An interior point method with a primal–dual quadratic barrier penalty function for nonlinear semidefinite programming [DOI]
- 13 Integrating optimization technique coupling an evolutionary algorithm and local search scheme
- 14 Hybrid optimization technique coupling an evolutionary algorithm and chaotic local search
Cited by 2
F. J. Lopez-Jaquez · 2016
A. A. Mousa, Salman M. Qassm, A. A. Alnefaie · 2020
Related research
- mcga: R Implementation of the Machine-coded Genetic Algorithms — shares topic coverage
- Studying the effect of Eliminating Repeated Individuals from the Population in a Genetic Algorithm: Solution Perspectives for the Travelling Salesman Problem — shares topic coverage
- Genetic Algorithm and Rough Sets Based Hybrid Approach for Economic Environmental Dispatch of Power Systems — shares topic coverage
- Enhanced Supply Chain Algorithm for ERP Systems Using ACO, Genetic, and Floyd-Warshall Algorithms — shares topic coverage
- Optimization Method for the Internal Distribution Network of a Photovoltaic Plant Using Genetic Algorithm — shares topic coverage
Article metrics
Real usage data collected on this platform.
38
Page views
0
PDF downloads
4
Outbound clicks
2
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.