Multi-Objective Optimization in Computational Intelligence

Dear Colleagues,

In many real-world applications, multiple optimization objectives are typically expected for decision making. For example, in a multirobot system, multirobot task allocation problems involve multiple optimization objectives, i.e., minimizing the total traveling time of robots and makespan. Because the optimization objectives conflict, multiple optimal solutions are expected; these are called Pareto optimal solutions. Optimization algorithms are required to find a set of optimal solutions. In recent years, multiobjective optimization problems have attracted increasing attention. Due to the adoption of population evolution, evolutionary algorithms are naturally employed to obtain a set of solutions for solving multiobjecitve optimization problems. Evolutionary algorithms have become a popular and vital method via which to solve multiobjective optimization problems. However, as the complexity of problems rises, existing evolutionary multiobjective algorithms meet challenges in terms of diversity and convergence. New algorithms are therefore developed to solve these complex application problems.  The scope of this Special Issue includes, but is not limited to, the following topics:

• Evolutionary algorithms;
• Swarm intelligence;
• Bio-inspired algorithms and their application;
• Machine learning assisted algorithms.

Dr. Xiao-Fang Liu
Guest Editor

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• multiobjective optimization
• evolutionary algorithms
• swarm intelligence
• machine learning