Local Fitness Landscape Exploration Based Genetic Algorithms
Local Fitness Landscape Exploration Based Genetic Algorithms
复制标题
DOI：
10.1109/access.2023.3234775
复制
发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Rahul Dubey;S. Hickinbotham;M. Price;A. Tyrrell
中科院分区：
文献类型：
--
作者：
Rahul Dubey;S. Hickinbotham;M. Price;A. Tyrrell
遗传算法（GAs）已被用于进化许多问题的最优/次最优解。当使用GAs进行演化解决方案时，适应度评估通常是计算成本最高的，这阻碍了研究人员将GAs应用于具有计算挑战性的问题。本文提出了一种基于局部适应度景观探索的子代生成方法，以提高搜索最优/次最优解的速度，并进化出更好的适应度解。提出的“基于适应度景观探索的遗传算法”（FLEX-GA）可以应用于单目标和多目标优化问题。分别对有约束和无约束的单目标和多目标基准问题进行了实验。在单目标问题上，将该算法与经典遗传算法和其他算法进行了性能比较。针对多目标基准问题，与NSGA-II等多目标优化算法进行了比较。最后，对八个现实世界的多目标优化问题进行了Pareto解的演化，并比较了NSGA-II的性能。实验结果表明，在大多数单目标和多目标问题上使用FLEX，搜索速度提高了50%以上，并且解决方案的质量也有所提高。这些结果充分证明了基于适应度景观近似的算法在解决现实世界优化问题中的适用性。
Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm” (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.