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In this paper, a chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm is proposed. The improved algorithm is used to solve the problems that the whale optimization algorithm's convergence accuracy and convergence speed are insufficient. Firstly, the proposed algorithm is initialized by a method based on chaotic maps and quadratic opposition-based learning strategy to obtain a population with better ergodicity. Secondly, by introducing an adaptive variable speed adjustment factor, each search link unites to form a negative feedback regulation network, thereby effectively balancing the algorithm's exploration ability and exploitation ability. Finally, 20 benchmark test functions and 3 complex constrained engineering optimization problems were used to conduct extensive tests on the improved algorithm. The results show that the improved algorithm has better performance than others in terms of convergence speed and convergence accuracy in a majority of cases, and can effectively jump out of the local optimum. [Display omitted] • Firstly, this paper proposed a quadratic Opposition-Base Learning strategy based on Bernoulli map, and successfully obtained a higher quality initial whale population. • Secondly, this paper proposed a new variable speed adjustment factor and a cooperative convergence factor, and combine them with individual fitness to form a negative feedback adjustment network. Experiments show that the algorithm has good self-adjusting ability. • Finally, the range of some of the original random numbers in whale optimization algorithm has been changed. This adjustment allows the algorithm to have a more variable search range. [ABSTRACT FROM AUTHOR]

The efficient operation and in-core fuel management of PWRs are of utmost importance. In the present work, a core reload optimization using Shuffled Frog Leaping (SFL) algorithm is addressed and mapped on nuclear fuel loading pattern optimization. SFL is one of the latest meta-heuristic optimization algorithms which is used for solving the discrete optimization problems and inspired from social behavior of frogs. The algorithm initiates the search from an initial population and carries forward to draw out an optimum result. This algorithm employs the use of memetic evolution by exchanging ideas between the members of the population in each local search. The local search of SFL is similar to particle swarm optimization (PSO) and applying shuffling process accomplishes the information exchange between several local searches to obtain an overall optimum result. To evaluate the proposed technique, Shekel's Foxholes and a VVER-1000 reactor are used as test cases to illustrate performance of SFL. Among numerous neutronic and thermal-hydraulic objectives necessary for a fuel management problem to reach an overall optimum, this paper deals with two neutronic objectives, i.e., maximizing effective multiplication factor and flattening power distribution in the core, to evaluate the capability of applying SFL algorithm for a fuel management problem. The results, convergence rate and reliability of the method are quite promising and show the potential and efficiency of the technique for other optimization applications in the nuclear engineering field. [ABSTRACT FROM AUTHOR]