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To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process. [ABSTRACT FROM AUTHOR]

Balancing the convergence and the diversity is one of the crucial researches in solving multi-objective problems (MOPs). However, the optimization algorithms are inefficient and require massive iterations. The convergence accuracy and the distribution of the obtained non-dominated solutions are defective in solving complex MOPs. To solve these problems, a novel adaptive multi-objective particle swarm optimization using a three-stage strategy (tssAMOPSO) is proposed in this paper. Firstly, an adaptive flight parameter adjustment is proposed to manage the states of the algorithm, switching between the global exploration and the local exploitation. Then, the three-stage strategy, including adaptive optimization, decomposition, and Gaussian attenuation mutation, is conducted by population in each iteration. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Furthermore, the convergence analysis of three-stage strategy is provided in detail. Finally, particles are equipped with memory interval to improve the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of non-dominated solutions directly. A series of benchmark instances, ZDT and DTLZ test suits, are used to verify the performance of tssAMOPSO. Several classical and state-of-the-art algorithms are employed for experimental comparisons. Experimental results show that tssAMOPSO outperforms the other algorithms and achieves admirable comprehensive performance. [ABSTRACT FROM AUTHOR]

Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, the SMA uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, we investigate an adaptive approach to decide whether opposition-based learning (OBL) will be used or not. Sometimes, the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The qualitative and quantitative analysis of AOSMA is reported using 29 test functions that consisting of 23 classical test functions and 6 recently used composition functions from the IEEE CEC 2014 test suite. The results are compared with state-of-the-art optimization methods. Results presented in this paper show that AOSMA's performance is better than other optimization algorithms. The AOSMA is evaluated using Wilcoxon's rank-sum test. It also ranked one in Friedman's mean rank test. The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems. [ABSTRACT FROM AUTHOR]

As a relatively new model, the artificial bee colony algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modifying the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on CEC2014, and CEC2015 benchmark suites, real-life problems. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching–Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results. [ABSTRACT FROM AUTHOR]