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A multimetric and multideme multiagent system for multiobjective optimization
Author(s) -
Tamouk Jamshid,
Acan Adnan
Publication year - 2018
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12175
Subject(s) - metric (unit) , multi objective optimization , computer science , metaheuristic , mathematical optimization , population , pareto principle , simulated annealing , particle swarm optimization , session (web analytics) , swarm behaviour , artificial intelligence , mathematics , engineering , operations management , demography , sociology , world wide web
This article proposes a multiagent system consisting of a number of multiobjective metaheuristic agents (namely, multiobjective genetic algorithm, strength Pareto evolutionary algorithm, differential evolution, simulated annealing, and particle swarm optimization) working toward to extract optimal or very close‐to‐optimal Pareto fronts using multiple performance metrics in a sessionwise manner. At the beginning of each session, the main population is divided into a number of subpopulations, and each of them is assigned to a particular agent. The system runs in consecutive sessions such that, at the beginning of a session, agents start running after being assigned with a subpopulation and return the optimized subpopulations together with the corresponding set of nondominated solutions at the end of the session. There are 3 multiobjective assessment metrics in use, and a different metric is considered for each session to measure the success of each metaheuristic agent. The evaluation of individual agents using a particular assessment metric is used in 2 ways: first, the number of fitness evaluations for each agent is adjusted based on their performance; second, the subpopulation improved by an individual agent might be rejected on the basis of its evaluation score. At the end of each session, individual subpopulations are merged to get the updated main population, whereas individual sets of nondominated solutions are combined to form the global Pareto front. In addition to the individual multiobjective metaheuristic agents, the system also contains a number of coordination and synchronization agents that run the whole system toward its objectives. The proposed system is tested using real‐valued multiobjective benchmark problems in 2009 IEEE Congress on Evolutionary Computation. Experimental results and statistical evaluations exhibited that the achieved success is better than many of state‐of‐the‐art algorithms.