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The differential evolution (DE) algorithm is widely regarded as one of the most influential evolutionary algorithms for addressing complex optimization problems. However, the fixed mutation strategy limits the adaptive ability of DE, and the lack of utilization of historical information limits the optimization ability of DE. In this paper, an indicator-based self-learning differential evolution algorithm (ISDE) is proposed. A jump out mechanism based on deep reinforcement learning is adopted to control the mutation intensity of the population. The neural network in the jump out mechanism is designed as a decision maker. The mutation intensity of the population is controlled by the neural network, and the neural network are trained by a double deep Q network algorithm based on the continuous data generated during the evolution process. A population range indicator (PRI) is utilized to describe individual differences in the population. A diversity maintenance mechanism is designed to maintain individual differences according to the value of PRI. The experimental results reveal that the comprehensive performance of ISDE is superior to comparison algorithms on CEC 2017 real-parameter numerical optimization. [ABSTRACT FROM AUTHOR]

The selection of an algorithm in the real world of the application domain is a challenging problem as no specific algorithm exists capable of solving all issues to a satisfactory requirement. Selecting a suitable algorithm presents major challenges such as solving problems requiring expert knowledge or trial-and-error algorithms, which have hindered advancements in this field. In this work, we introduce a novel method that uniquely addresses these challenges by integrating hyper-heuristic and Q-learning mechanism techniques. A selection hyper-heuristic algorithm with Q-learning (QLSHH) is proposed to select appropriate low-level heuristic (LLH) for the computation stages of the optimization process. The Q-learning mechanism guided by the feedback of the solution state was designed according to the environment. Four low-level heuristics (LLHs) were proposed according to the optimization mechanism for continuous optimization problems. The QLSHH learns the successful experience in the optimization process through Q-learning to select the appropriate LLH at each decision point. The results tested on the CEC 2017 and CEC 2020 benchmark suite show that the QLSHH outperforms the other nine comparison algorithms on 50% of the functions and the experimental results of algorithm complexity show that the proposed algorithm is the fastest compared with other algorithms. • A hyper-heuristic algorithm using Q-learning as a high-level strategy is designed. • An online learning mechanism is designed to guide the algorithm to select strategy. • Q-learning strategy makes QLSHH intelligent in selecting LLH at optimization process. • QLSHH selects the appropriate strategy through learning the successful experience. • Four LLHs are extracted and designed to balance exploration and exploitation. [ABSTRACT FROM AUTHOR]

The large-scale real value problem in continuous and discrete optimization problems is a challenging issue for researchers and practitioners in the manufacturing domain. An improved artificial bee colony algorithm (ABC) combined with multi-agent reinforcement learning (called MARLABC) is presented for addressing the large-scale real value optimization problem in this study. Two stages including the training and the testing are introduced in the MARLABC via the multi-agent central controller to improve the convergence speed and local exploitation capability of the algorithm. The optimal strategy pool is constructed by training procedures via the multi-agent central controller with Q-learning mechanism. The effective strategy is selected from the optimal strategy pool for each agent during the testing process in the multi-agent central controller. The elite agents in the training population are reserved to generate the testing population to guide the search. The MARLABC algorithm is compared with the ABC variants and state-of-art algorithms on CEC 2017 benchmark problems. The stability and effectiveness of the MARLABC algorithm are confirmed by the experimental results. [ABSTRACT FROM AUTHOR]