Nonlinear continuous multi-response problems: a novel two-phase hybrid genetic based metaheuristic

Authors:
Saeid Fallah-Jamshidi;Maghsoud Amiri;Neda Karimi
Affiliations:
Department of Industrial and Mechanical Engineering, Qazvin Islamic Azad University, Qazvin, Iran;Department of Industrial Management, Management and Accounting Faculty, Allameh Tabatabaei University, Tehran, Iran;Department of Industrial and Mechanical Engineering, Qazvin Islamic Azad University, Qazvin, Iran
Venue:
Applied Soft Computing
Year:
2010

Citing 7
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
An updated survey of GA-based multiobjective optimization techniques

ACM Computing Surveys (CSUR)
Compaction of Symbolic Layout Using Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Contrasting Structured and Random Permutation Flow-Shop Scheduling Problems: Search-Space Topology and Algorithm Performance

INFORMS Journal on Computing
Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach

Expert Systems with Applications: An International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Generally the most real world production systems are tackling several different responses and the problem is optimizing these responses concurrently. This study strives to present a new two-phase hybrid genetic based metaheuristic for optimizing nonlinear continuous multi-response problems. Premature convergence and getting stuck in local optima, which makes the algorithm time consuming, are common problems dealing with genetic algorithms (GAs). So we hybridize GA with a clustering approach and particle swarm optimization algorithm (PSO) to make a balanced relationship between time consuming and premature termination. The proposed algorithm also tries to find Ideal Points (IPs) for response functions. IPs are considered as improvement measures that determine when PSO should start. PSO based local search exploit Pareto archive solutions to enhance performance of the algorithm by expanding the search space. Since there is no standard benchmark in this field, we use two case studies from distinguished paper in multi-response optimization and compare the results with some of the mentioned algorithms in the literature. Results show the outperformance of the proposed algorithm than all of them.