Remora Optimization Algorithm Combining Joint Opposite Selection and Host Switching

The remora optimization algorithm (ROA) is a meta heuristic optimization algorithm proposed in 2021. It simulates the behavior of parasitic attachment to the host, empirical attack and host foraging in the ocean. The structure of ROA is simple and easy to implement, but the overall situation is slightly insufficient, which easily leads to ROA’s slow convergence speed and even difficult convergence in the later period. To solve the above problems, host switching mechanism is added in the exploration phase, and new host beluga is introduced to improve the exploration ability of original ROA. At the same time, through adding joint opposite selection strategy, the ability of the algorithm to jump out of the local optimum is enhanced, and the comprehensive optimization performance of the algorithm is further improved. Through the above improvements, an improved remora optim-ization algorithm (IROA) is proposed, which integrates the joint opposite selection and host switching mechanism. In order to verify the performance and improvement advantages of IROA, IROA is compared with the original ROA, six typical original algorithms and four improved algorithms on ROA. Experimental results of CEC2020 standard test function show that IROA has stronger optimization ability and higher convergence accuracy. Finally, the advantages and engineering applicability of the improved algorithm are further verified by solving the car crashworthiness design problem

A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results

Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem

Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora in the ocean. However, the performance of ROA is not very good. Considering the habits of the remora that rely on the host to find food, and in order to improve the performance of the ROA, we designed a new host-switching mechanism. By adding new a host-switching mechanism, joint opposite selection, and restart strategy, a modified remora optimization algorithm (MROA) is proposed. We use 23 standard benchmark and CEC2020 functions to test the performance of MROA and compare them with eight state-of-art optimization algorithms. The experimental results show that MROA has better-optimized performance and robustness. Finally, the ability of MROA to solve practical problems is demonstrated by five classical engineering problems