Non-Dominated Sorting Genetic Algorithm Based on Altruism for Solving Multi-Objective Optimization Problems
Multiobjective optimization problems are typically complex problems. Multiobjective optimization aim to find Pareto-optimal solutions because no single unique solution is the best. We can solve multiob- jective optimization problems mathematically or by applying heuristics or metaheuristic
algorithms. Metaheuristic algorithms have big advantages in dealing with multiobjective optimization problems. Revised non-dominated sorting genetic algorithm (NSGA-II) is an effective algorithm for finding pareto optimal front. In this paper, we enhanced the performance of NSGA-II for multiobjective
optimization by adding altruism. Altruism here depends on gene rather than chromosome. The proposed algorithm named, the non-dominated sorting genetic algorithm with gene based altruism (NSGA-II-GBA). The proposed algorithm favored over other algorithms because of its advantages in reducing
number of generations to obtain optimal solutions, time and space complexity. And also increased search diversity and convergence speed of multiobjective optimization problems. In this paper we describe the proposed algorithm for solving multiobjective optimization problems. In addition, we
also validate the algorithm against a set of multiobjective test functions and two real life engineering design problems are also solved to validate the efficiency and, applicability of the proposed algorithm in solving such problems.
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Genes Underlying Altruism;
Revised Non-Dominated Sorting
Document Type: Research Article
Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, El-Zera Square, Zagazig, Sharqiyah, 44519, Egypt
Department of Operations Research, Faculty of Computers and Information, Menoufia University, Menoufia, Shebin-El-Kome, 32511, Egypt
Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, El-Zera Square, Zagazig, Sharqiyah, 44519, Egypt
August 1, 2016
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