Cooperative Multi-agent Systems for Single and Multi-objective Optimization

Solving combinatorial and real-parameter optimization problems is an important challenge in all engineering applications. Researchers have been extensively solving these problems using evolutionary computations. In this thesis, three new multi-agent architectures are designed and utilized in order to solve combinatorial and realparameter optimization problems. First architecture introduces a novel learning-based multi-agent system (LBMAS) for solving combinatorial optimization problems in which all agents cooperate by acting on a common population and a two-stage archive containing promising fitness-based and positional-based solutions found so far. Metaheuristics as agents perform their own method individually and afterwards share their outcomes with others. In this system, solutions are modified by all running metaheuristics and the system learns gradually how promising metaheuristics are, in order to apply them based on their effectiveness. In the second architecture, a novel multi-agent and agent interaction mechanism for the solution of single objective type real-parameter optimization problems is proposed. The proposed multi-agent system includes several metaheuristics as problem solving agents that act on a common population containing the frontiers of search process and a common archive keeping the promising solutions extracted so far. Each session of the proposed architecture includes two phases: a tournament among all agents to determine the currently best performing agent and a search procedure conducted by the winner. The proposed multi-agent system is experimentally evaluated using the well-known CEC2005 benchmark problems set. The third architecture presents a creative multi-agent and dynamic multi-deme architecture based on a novel collaboration mechanism for the solution of multiobjective real-parameter optimization problems. The proposed architecture comprises a number of multi-objective metaheuristic agents that act on subsets of a population based in a cyclic assignment order. This multi-agent architecture works iteratively in sessions including two consecutive phases: in the first phase, a population of solutions is divided into subpopulations based on the dominance ranks of its elements. In the second phase, each multi-objective metaheuristic is assigned to work on a subpopulation based on a cyclic or round-robin order. The proposed multiagent system is experimentally evaluated using the well-known CEC2009 multiobjective optimization benchmark problems set. Analysis of the experimental results showed that the proposed architectures achieve better performance compared to majority of their state-of-the-art competitors in almost all problem instances. Keywords: Multi-agent systems, Metaheuristics, Combinatorial Optimization, Multiprocessor Scheduling, Agent Interactions, Multi-objective Optimization, Pareto Optimality