Multi-operator based evolutionary algorithms for solving constrained optimization problems

• Published in: Computers & operations research Vol. 38; no. 12; pp. 1877 - 1896
• Main Authors: Elsayed, Saber M., Sarker, Ruhul A., Essam, Daryl L.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-12-2011; Elsevier; Pergamon Press Inc
• Subjects: Algorithms; Applied sciences; Comparative analysis; Constrained optimization; Constraints; Differential evolution; Evolution; Evolutionary algorithms; Exact sciences and technology; Genetic algorithm; Genetic algorithms; Mathematical programming; Operational research and scientific management; Operational research. Management science; Operations research; Operator ensemble; Operators; Optimization; Optimization algorithms; Searching; Studies; Operator ensemble; Differential evolution; Genetic algorithm; Constrained optimization; Evolutionary algorithm; Variability; Problem solving; Coverage; Set constraint; Competitive algorithms
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2011.03.003

Over the last two decades, many sophisticated evolutionary algorithms have been introduced for solving constrained optimization problems. Due to the variability of characteristics in different COPs, no single algorithm performs consistently over a range of problems. In this paper, for a better coverage of the problem characteristics, we introduce an algorithm framework that uses multiple search operators in each generation. The appropriate mix of the search operators, for any given problem, is determined adaptively. The framework is tested by implementing two different algorithms. The performance of the algorithms is judged by solving 60 test instances taken from two constrained optimization benchmark sets from specialized literature. The first algorithm, which is a multi-operator based genetic algorithm (GA), shows a significant improvement over different versions of GA (each with a single one of these operators). The second algorithm, using differential evolution (DE), also confirms the benefit of the multi-operator algorithm by providing better and consistent solutions. The overall results demonstrated that both GA and DE based algorithms show competitive, if not better, performance as compared to the state of the art algorithms.