A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization
A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization
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DOI：
10.1109/tevc.2008.920671
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发表时间：
2009-02-01
影响因子：
14.3
通讯作者：
Tan, Kay Chen
中科院分区：
文献类型：
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作者：
Goh, Chi-Keong;Tan, Kay Chen
除了需要满足多个相互竞争的目标之外，许多实际应用也是动态的，需要优化算法来跟踪随时间变化的最优值。本文提出了一种新的协同进化范式，该范式混合了自然界中观察到的竞争和合作机制，以解决多目标优化问题并在动态环境中跟踪帕累托前沿。竞争-合作协同进化的主要思想是让优化问题的分解过程适应和出现，而不是在进化优化过程开始时手工设计和固定。特别是，每个物种亚群将竞争代表多目标问题的特定子组成部分，而最终的获胜者将合作进化以获得更好的解决方案。通过这种竞争与合作的迭代过程，不同物种子种群根据特定时刻的优化要求对各个子组件进行优化，使协同进化算法能够同时处理静态和动态多目标问题。竞争合作协同进化算法（COEA）在静态环境中的有效性在不同基准问题上针对各种多目标进化算法进行了验证，这些基准问题的特点是局部最优性、不连续性、非凸性和高维性方面的各种困难。此外，还进行了广泛的研究来检验动态 COEA (dCOEA) 在动态环境中跟踪帕累托前沿随时间变化的能力。
In addition to the need for satisfying several competing objectives, many real-world applications are also dynamic and require the optimization algorithm to track the changing optimum over time. This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multiobjective optimization problems and to track the Pareto front in a dynamic environment. The main idea of competitive-cooperative coevolution is to allow the decomposition process of the optimization problem to adapt and emerge rather than being hand designed and fixed at the start of the evolutionary optimization process. In particular, each species subpopulation will compete to represent a particular subcomponent of the multiobjective problem, while the eventual winners will cooperate to evolve for better solutions. Through such an iterative process of competition and cooperation, the various subcomponents are optimized by different species subpopulations based on the optimization requirements of that particular time instant, enabling the coevolutionary algorithm to handle both the static and dynamic multiobjective problems. The effectiveness of the competitive-cooperation coevolutionary algorithm (COEA) in static environments is validated against various multiobjective evolutionary algorithms upon different benchmark problems characterized by various difficulties in local optimality, discontinuity, nonconvexity, and high-dimensionality. In addition, extensive studies are also conducted to examine the capability of dynamic COEA (dCOEA) in tracking the Pareto front as it changes with time in dynamic environments.