Abstract:

The optimization process of Remora Optimization Algorithm （ROA） includes three modes： attaching to host， empirical attack and host foraging， and the exploration ability and exploitation ability of this algorithm are relatively strong. However， because the original algorithm switches the host through empirical attack， it will lead to the poor balance between exploration and exploitation， slow convergence and being easy to fall into local optimum. Aiming at the above problems， a Modified ROA （MROA） based on chaotic host switching mechanism was proposed. Firstly， a new host switching mechanism was designed to better balance the abilities of exploration and exploitation. Then， in order to diversify the initial hosts of remora， Tent chaotic mapping was introduced for population initialization to further optimize the performance of the algorithm. Finally， MROA was compared with six algorithms such as the original ROA and Reptile Search Algorithm （RSA） in the CEC2020 test functions. Through the analysis of the experimental results， it can be seen that the best fitness value， average fitness value and fitness value standard deviation obtained by MROA are better than those obtained by ROA， RSA， Whale Optimization Algorithm （WOA）， Harris Hawks Optimization （HHO） algorithm， Sperm Swarm Optimization （SSO） algorithm， Sine Cosine Algorithm （SCA）， and Sooty Tern Optimization Algorithm （STOA） by 28%， 33%， and 12% averagely and respectively. The test results based on CEC2020 show that MROA has good optimization ability， convergence ability and robustness. At the same time， the effectiveness of MROA in engineering problems was further verified by solving the design problems of welded beam and multi-plate clutch brake.

Key words: Remora Optimization Algorithm (ROA), host switching mechanism, Tent chaotic mapping, benchmark function test, engineering problem solving

摘要：

?鱼优化算法（ROA）的寻优过程包括依附宿主、经验攻击和宿主觅食3种模式，它的探索能力与开发能力较强；但原始算法通过经验攻击切换宿主，导致探索与开发之间平衡较差、收敛较慢且容易陷入局部最优。针对上述问题，提出了一种基于混沌宿主切换机制的改进?鱼优化算法（MROA）。首先，设计一种新的宿主切换机制，以更好地平衡探索和开发的能力；然后，为了使?鱼初始宿主多样化，引入Tent混沌映射进行种群初始化，进一步优化算法的性能；最后，将MROA与原始ROA和爬行动物搜索算法（RSA）等6种算法在CEC2020测试函数上进行对比实验。分析实验结果可知，MROA求得的最优适应度值、平均适应度值和适应度值标准差分别比ROA、RSA、鲸鱼优化算法（WOA）、哈里斯鹰优化（HHO）算法、精子群优化（SSO）算法、正余弦算法（SCA）和乌燕鸥优化算法（STOA）平均提高了28%、33%和12%。基于CEC2020的测试结果表明，MROA具有良好的寻优能力、收敛能力和鲁棒性；同时，通过求解焊接梁设计问题和多片式离合器制动器设计问题，进一步验证了MROA在工程问题中的有效性。

关键词: ?鱼优化算法, 宿主切换机制, Tent混沌映射, 基准函数测试, 工程问题求解