=================================== (1) the complete title of the paper ----------------------------------- Multi-objective optimisation by co-operative co-evolution (2) the name, physical mailing address, e-mail address, and phone number ------------------------------------------------------------------------ Kuntinee Maneeratana, kuntinee.m@chula.ac.th Department of Mechanical Engineering, Chulalongkorn University, Phaya Thai Road, Pathum Wan, Bangkok 10330, THAILAND phone: +66 2 218-6639 Kittipong Boonlong, kittipong_toy@yahoo.com Department of Mechanical Engineering, Chulalongkorn University, Phaya Thai Road, Pathum Wan, Bangkok 10330, THAILAND phone: +66 4 643-5715 Nachol Chaiyaratana, nchl@kmitnb.ac.th Research and Development Center for Intelligent Systems, King Mongkut's Institute of Technology North Bangkok, 1518 Piboolsongkram Road, Bangsue, Bangkok 10800, THAILAND phone: +66 2 913-2500 ext 8410 (3) the name of the corresponding author ---------------------------------------- Nachol Chaiyaratana (4) the abstract of the paper ----------------------------- This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multi- objective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a non- dominated sorting genetic algorithm (NSGA) and a controlled elitist non- dominated sorting genetic algorithm (CNSGA). The resulting algorithms can be referred to as co-operative co-evolutionary multi-objective optimisation algorithms or CCMOAs. The CCMOAs are benchmarked against the EMOAs in seven test problems. The first six problems cover different characteristics of multi-objective optimisation problems, namely convex Pareto front, non-convex Pareto front, discrete Pareto front, multi- modality, deceptive Pareto front and non-uniformity of solution distribution. In contrast, the last problem is a two-objective real-world problem, which is generally referred to as the continuum topology design. The results indicate that the CCMOAs are superior to the EMOAs in terms of the solution set coverage, the average distance from the non-dominated solutions to the true Pareto front, the distribution of the non-dominated solutions and the extent of the front described by the non-dominated solutions. (5) a list containing one or more of the eight letters ------------------------------------------------------ (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. (G) The result solves a problem of indisputable difficulty in its field. (6) a statement stating why the result satisfies that criteria -------------------------------------------------------------- The paper describes a novel integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four multi-objective optimisation techniques namely a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a non-dominated sorting genetic algorithm (NSGA) and a controlled elitist non-dominated sorting genetic algorithm (CNSGA). The resulting combined algorithms have been proven to be highly efficient and improve the solutions and convergence rates in two-objective problems. Specifically, the ZDT1-ZDT6 benchmark problems have been successfully solved using the proposed approaches. In addition, the ZDT5 result shows remarkable improvements with respect to available works. (7) a full citation of the paper -------------------------------- Maneeratana, K., Boonlong, K. and Chaiyaratana, N. (2004). Multi-objective optimisation by co-operative co-evolution. The 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII) - Lecture Notes in Computer Science 3242, 18-22 September 2004, Birmingham, UK, 772-781.