
In this paper a new concept of ranking among the solutions of the same front, along with elite preservation mechanism and ensuring diversity through the nearest neighbor method is proposed for multi-objective genetic algorithms. This algorithm is applied on a set of benchmark multi-objective test problems and the results are compared with that of NSGA-II (a similar algorithm). The proposed algorithm is seen to over perform the existing algorithm. More specifically, the new approach has been used to solve the deceptive multi-objective optimization problems in a better way.
Pareto optimality, multi-objective optimization, Computer Science & Automation (Formerly, Learning and adaptive systems in artificial intelligence, evolutionary computing, School of Automation), Approximation methods and heuristics in mathematical programming, Nonnumerical algorithms, Multi-objective and goal programming, genetic algorithms
Pareto optimality, multi-objective optimization, Computer Science & Automation (Formerly, Learning and adaptive systems in artificial intelligence, evolutionary computing, School of Automation), Approximation methods and heuristics in mathematical programming, Nonnumerical algorithms, Multi-objective and goal programming, genetic algorithms
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