Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An improved immune genetic algorithm for multiobjective optimization
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
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The weight-based multiobjective evolutionary algorithms have been criticized mainly for the following aspects: (1) difficulty in finding Pareto-optimal solutions in problems having nonconvex Pareto-optimal region, and (2) non-elitism approach for most cases, and (3) difficulty in generating uniformly distributed Pareto-optimal solutions. In this paper, we propose a weight-based multiobjective immune genetic algorithm(MOIGA), which alleviates all the above three difficulties. In this proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function. An immune operator is adopted to increase the diversity of the population. Specifically, a new mate selection approach called tournament selection algorithm with similar individuals (TSASI ) and a new environmental selection approach named truncation algorithm with similar individuals (TASI ) are presented. Simulation results show MOIGA outperforms NSGA-II and RWGA.