Oztas Gulin Zeynep, Erdem Sabri
Department of Business Administration, Pamukkale University, 20160 Denizli, Turkey.
Department of Business, Dokuz Eylul University, 35390 İzmir, Turkey.
Neural Comput Appl. 2023;35(10):7635-7658. doi: 10.1007/s00521-022-08058-8. Epub 2022 Dec 9.
This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature.
本文提出了一种基于种群的进化计算模型，用于解决连续约束非线性优化问题。主要目标是在特定问题类型中获得更好的解决方案，而不考虑隐喻和相似性。所提出的算法假设候选解相互作用以获得更好的适应度值。通过考虑欧几里得距离，候选解之间的相互作用受到最近邻的限制。此外，禁忌搜索算法和精英选择方法启发了所提出算法的内存使用。此外，该算法基于乘法惩罚方法的原理构建，该方法考虑满意度、约束的总偏差和目标函数值，以很好地处理连续约束问题。该算法的性能通过实际工程设计优化基准问题进行评估，这些问题属于进化优化研究人员最常用的案例。实验结果表明，与文献中发表的其他算法相比，所提出的算法产生了令人满意的结果。本研究的主要目的是提供一种算法，该算法能够达到最知名的解值，而不是通过新的隐喻复制现有算法。我们构建了具有最佳特征组合的所提出算法，以获得更好的解决方案。与类似算法不同，本研究处理了约束工程问题。因此，其目的是证明所提出的算法比类似算法和文献中开发的其他算法产生更好的结果。
