Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization using a Pareto differential evolution approach
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Clonal selection algorithm for dynamic multiobjective optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Clonal selection with immune dominance and anergy based multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An organizational coevolutionary algorithm for classification
IEEE Transactions on Evolutionary Computation
A multiagent evolutionary algorithm for constraint satisfaction problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new immune clone algorithm to solve the constrained optimization problems
WSEAS Transactions on Computers
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In this paper, we introduce a new multiobjective optimization (MO) algorithm to solve ZDT test problems using the immune clonal principle. This algorithm is termed Immune Clonal MO Algorithm (ICMOA). In ICMOA, the antibody population is split into nondominated antibodies and dominated antibodies. Meanwhile, the nondominated antibodies are allowed to survive and to clone and the nonuniform mutation is adopted. Two metrics proposed by K. Deb et al. are adopted to measure the extent of convergence to a known set of Pareto-optimal solutions and the extent of spread achieved among the obtained solutions. Our algorithm is compared with another algorithm that is representative of the state-of-the-art in evolutionary multiobjective optimization–NSGA-II. Simulation results on ZDT test problems show that ICMOA, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to NSGA-II.