MOCCA-II: A multi-objective co-operative co-evolutionary algorithm

Most real-world problems naturally involve multiple conflicting objectives, such as in the case of attempting to maximize both efficiency and safety of a working environment. The aim of multi-objective optimization algorithms is to find those solutions that optimize several components of a vector of objective functions simultaneously. However, when objectives conflict with each other, the multi-objective problem does not have a single optimal solution for all objectives simultaneously. Instead, algorithms attempt to search for the set of efficient solutions, known as the global non-dominated set, that provides solutions that optimally trade-off the objectives. The final solution to be adopted from this set would depend on the preferences of the decision-makers involved in the process. Hence, a decision-maker is typically interested in knowing as many potential solutions as possible. In this paper, we propose an extension to a previous piece of work on multi-objective cooperative coevolution algorithms (MOCCA). The idea was motivated with a practical problem in air traffic management to design terminal airspaces. MOCCA and a further study that was done on a distributed environment for MOCCA, were found to fit the need for the application. We systematically questioned key components of this algorithm and investigated variations to identify a better design. This paper summarizes this systematic investigation and present the resultant new algorithm: multi-objective co-operative co-evolutionary algorithm II (MOCCA-II).

[1]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[2]  César Hervás-Martínez,et al.  Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks) , 2002, Neural Networks.

[3]  Jia Li-Min,et al.  A Multi-objective Cooperative Coevolutionary Algorithm for Constructing Accurate and Interpretable Fuzzy systems , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[4]  D. Sofge,et al.  A blended population approach to cooperative coevolution for decomposition of complex problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[6]  Samir W. Mahfoud.  Niching methods for genetic algorithms , 1996 .

[7]  Xiaodong Li,et al.  A Cooperative Coevolutionary Multiobjective Algorithm Using Non-dominated Sorting , 2004, GECCO.

[8]  Kenneth A. De Jong,et al.  The Effects of Representational Bias on Collaboration Methods in Cooperative Coevolution , 2002, PPSN.

[9]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[10]  Peter J. Fleming,et al.  Multiobjective genetic algorithms made easy: selection sharing and mating restriction , 1995 .

[11]  R. Paul Wiegand,et al.  An empirical analysis of collaboration methods in cooperative coevolutionary algorithms , 2001 .

[12]  Kay Chen Tan,et al.  A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[13]  H. Abbass.  The self-adaptive Pareto differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[15]  Kay Chen Tan,et al.  A distributed Cooperative coevolutionary algorithm for multiobjective optimization , 2006, IEEE Transactions on Evolutionary Computation.

[16]  E. F. Khor,et al.  An Evolutionary Algorithm with Advanced Goal and Priority Specification for Multi-objective Optimization , 2011, J. Artif. Intell. Res..

[17]  Xin Yao,et al.  Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..

[18]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[19]  Xin Yao,et al.  Multilevel cooperative coevolution for large scale optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[20]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[21]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[22]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[23]  Tong Heng Lee,et al.  A cooperative coevolutionary algorithm for multiobjective optimization , 2003, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[24]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[25]  Zhenyu Yang,et al.  Large-Scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning , 2010, PPSN.

[26]  Z. Xing,et al.  On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm , 2007 .

[27]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[28]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[29]  Nachol Chaiyaratana,et al.  Multi-objective Co-operative Co-evolutionary Genetic Algorithm , 2002, PPSN.

[30]  Pascal Bouvry,et al.  Multi-objective Cooperative Coevolutionary Evolutionary Algorithms for Continuous and Combinatorial Optimization , 2011, Intelligent Decision Systems in Large-Scale Distributed Environments.

[31]  Hussein A. Abbass,et al.  I. Background , 2022 .