A Novel Cooperative Differential Evolution with Distance Ranking Strategy for Multiobjective Optimization

• Main Authors: Yu-Qing Huang, 黃玉清
• Other Authors: Shu-Kai S. Fan
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/62796225042681680061

碩士 === 元智大學 === 工業工程與管理學系 === 97 === Multiobjective optimization is ubiquitous around human lives. This type of optimization involves the simultaneously optimization of multiple noncommensurable and often competing objectives. In single-objective optimization, there exists a global optimum, while in the multiobjective case, no single optimal solution but rather a set of solutions, called the Pareto-optimal solutions exist. Thus, the goal of multiobjective optimization is to generate a set of nondominated solutions as an approximation to the true Pareto-optimal front. However, the majority of problems of this kind are very hard to solve because the decision space can not directly map to the objective space. In addition, the complexity of the decision space is unknown. In recent years, evolutionary computations (ECs) have been recognized to be well suited for multiobjective optimization. This thesis presents a new algorithm, called “cooperative multiobjective differential evolution” (C-MODE), which allows the differential evolution (DE) algorithm to be capable of dealing with multiobjective optimization problems. The cooperative behavior is a recent trend in evolutionary computation that can help to make the search more efficient. In this thesis, the cooperative concept is incorporated as to enhance the search performance by means of two cooperative subpopulations, which perform the general DE and local search in DE, respectively. To conduct the local search, a unique method is proposed to enrich the exploratory capabilities. The convergence (i.e., accuracy) and diversity (uniformity and extensibility) are the main tasks in solving multiobjective optimization problems. In C-MODE, the distance ranking strategy (DRS) is proposed to maintain diversity. Additionally, a special mutation operator is opted to drive the search toward favorable directions and accelerate the search. The proposed algorithm is validated using several test functions and metrics taken from the open literature of evolutionary multiobjective optimization. The proposed algorithm is compared against four well-known multiobjective optimizers, SPEA2, NSGAII, PAES and MOPSO. Simulation results indicate that the algorithm is very competitive with the four well-known algorithms and shows great promise in dealing with complicated problems. Thus, C-MODE can be considered a successful alternative to solve multiobjective optimization problems. Key Words: Differential evolution; Multiobjective optimization; Pareto optimality; Cooperative behavior.