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Multiagent evolutionary computation (MAEC) is a new paradigm to efficiently solve a range of complex problems, by combining the advantages of evolutionary computation (EC) and multiagent systems (MAS). In general, there are three categories in MAEC: 1) “agent based EC” incorporates characteristics of intelligent agents, and adopts techniques in MAS to enhance the performance of EC; 2) “evolutionary computation based MAS” utilizes evolutionary techniques to design self-learning agents, to improve the efficiency of interactions between agents, and to increase the effectiveness of the whole system; 3) “the sequential or embedded approach of EC and MAS” employs EC and MAS in either a sequential order or an embedded fashion. The MAEC approaches have been successfully applied to solve benchmark and real world problems. However, most studies in the first category of MAEC focus only on single-objective optimization problems. In the second group of MAEC, the information communicated between agents is often assumed to be truthful. That is, the agents in MAS are assumed to be honest and cooperative. In addition, the two components of EC and MAS have not been integrated effectively in the third direction of MAEC. Thus, the ability of MAEC to solve complex problems has not been fully exploited by the previous studies. In view of this, this thesis proposes three approaches in the respective categories of MAEC for solving three complex problems, which are multiobjective optimization problems (MOPs), robustness of trust in MAS based e-marketplaces and traffic management in transportation systems. Firstly, a multiagent evolutionary framework based on trust is proposed to solve MOPs. This work lies into the first category of MAEC. In particular, the trust concept that is popular in MAS is adopted for measuring the dynamic competency of services (i.e., the pairs of evolutionary operators and control parameters) from generation to

Multiagent evolutionary computation for complex problems