Improving the non-dominated sorting genetic algorithm using a gene-therapy method for multi-objective optimization |
| |
| Institution: | 1. Department of Electrical Engineering and Computer Science, Case Western Reserve University,10900 Euclid Ave., Cleveland, OH 44106, USA;2. Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., 15875-4413 Tehran, Iran;1. Department of Mathematics and Physics, Università del Salento, via per Arnesano, 73100 Lecce, Italy;2. Department of Innovation Engineering, Università del Salento, via per Monteroni, 73100 Lecce, Italy;3. Department of Energy, Politecnico di Milano, via Lambruschini 4, 20156 Milano, Italy;1. Department of Mechanical Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran;2. School of Engineering, Tarbiat Modarres University, P.O. Box 14115-179, Tehran, Iran;1. Department of Civil and Environmental Engineering of Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;2. School of Computing, Engineering and Mathematics, University of Western Sydney, NSW 2753, Australia;1. Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University, Nanjing 210096, China;2. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China |
| |
| Abstract: | The non-dominate sorting genetic algorithmic-II (NSGA-II) is an effective algorithm for finding Pareto-optimal front for multi-objective optimization problems. To further enhance the advantage of the NSGA-II, this study proposes an evaluative-NSGA-II (E-NSGA-II) in which a novel gene-therapy method incorporates into the crossover operation to retain superior schema patterns in evolutionary population and enhance its solution capability. The merit of each select gene in a crossover chromosome is estimated by exchanging the therapeutic genes in both mating chromosomes and observing their fitness differentiation. Hence, the evaluative crossover operation can generate effective genomes based on the gene merit without explicitly analyzing the solution space. Experiments for nine unconstrained multi-objective benchmarks and four constrained problems show that E-NSGA-II can find Pareto-optimal solutions in all test cases with better convergence and diversity qualities than several existing algorithms. |
| |
| Keywords: | Genetic algorithm Gene therapy Evaluative crossover Multi-objective optimization |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
