Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems  

作　　者：Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah 

机构地区：[1]School of Mathematics,Statistics,and Computer Science,University of KwaZulu-Natal,King Edward Avenue,Pietermaritzburg Campus,Pietermaritzburg,3201,KwaZulu-Natal,South Africa [2]Department of Computer Science,Federal University of Lafia,Lafia,950101,Nigeria [3]Unit for Data Science and Computing,North-West University,11 Hoffman Street,Potchefstroom,2520,South Africa [4]Sorbonne Center of Artificial Intelligence,Sorbonne University-Abu Dhabi,38044,Abu Dhabi,United Arab Emirates [5]Hourani Center for Applied Scientific Research,Al-Ahliyya Amman University,Amman,19328,Jordan [6]Faculty of Information Technology,Middle East University,Amman,11831,Jordan [7]Faculty of Information Technology,Applied Science Private University,Amman,11931,Jordan [8]School of Computer Sciences,Universiti Sains Malaysia,11800,Pulau Pinang,Malaysia

出　　处：《Journal of Bionic Engineering》2023年第3期1263-1295,共33页仿生工程学报（英文版）

摘　　要：This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关 键 词：Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]