نوع مقاله : مقاله پژوهشی
نویسندگان
دانشکده فناوری اطلاعات و مهندسی کامپیوتر، دانشگاه شهید مدنی آذربایجان، تبریز، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The advancement of technology and the emergence of multi-objective optimization problems in various scientific domains have led to the research and presentation of new meta-heuristic algorithms to solve such problems. Although these algorithms have been able to find a relatively good approximation of the optimal Pareto front, but a complete optimization has not been carried out yet. In this paper, to increase the optimality of the generated Pareto front, we present a multi-objective version of the city council evolution algorithm (CCE) called the multi-objective city council evolution algorithm (MOCCE). In the presented algorithm, an archive with a fixed size is considered for storing and retrieving optimal Pareto solutions. This archive is used to define the hierarchical structure of city councils and to simulate its evolution in multi-objective search spaces. The efficiency of MOCCE algorithm has been evaluated on 18 well-known multi-objective test functions known as UF and IMOP and with the results of multi-objective ant lion optimization (MOALO), multi-objective orthogonal mould algorithm (MOSMA) and multi-objective artificial hummingbird optimization algorithms (MOAHA) have been compared. According to the results of the Friedman's mean rank test, in all UF test functions, MOCCE ranks first among all compared algorithms in terms of generation distance (GD), inverse generation distance (IGD) and maximum spread (MS) criteria. Also, this algorithm takes the first rank in all IMOP test functions in terms of GD criterion and the second rank in terms of IGD and MS criteria
کلیدواژهها [English]
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