Refractionlearningbased whale optimization algorithm for highdimensional problems and parameter estimation of PV model Abstract   Whale optimization algorithm (WOA) is a relatively new metaheuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving highdimensional optimization problems. The proposed RLWOA adopts a modified conversion parameter update rule that relies on Logistic model to balance between diversity and convergence during the search process, and a new refractionlearning strategy based on the principle of refraction of light is proposed to help the population jump out of a local optimum. The experiments on a set of benchmark test functions with various features, i.e., 12 widely used benchmark functions with 100, 1000, and 10000 dimensions, two practical engineering design problems, and parameter estimation problem of photovoltaic model. The comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other stateoftheart metaheuristic algorithms for solving highdimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.

An Adaptive Bayesian Approach to SurrogateAssisted Evolutionary Multiobjective Optimization Abstract   Surrogate models have been widely used for solving computationally expensive multiobjective optimization problems (MOPs). The efficient global optimization (EGO) algorithm, a Bayesian approach to surrogateassisted optimization, has become very popular in surrogateassisted evolutionary optimization. In this paper, we propose an adaptive Bayesian approach to surrogateassisted evolutionary algorithm to solve expensive MOPs. The main idea is to tune the hyperparameter in the acquisition function according to the search dynamics to determine which candidate solutions are to be evaluated using the expensive real objective functions. In addition, the sampling selection criterion switches between an angle based distance and an anglepenalized distance over the course of optimization to achieve a better balance between exploration and exploitation. The performance of the proposed algorithm is examined on a set of benchmark problems and an airfoil design optimization problem using a maximum of 300 real fitness evaluations. Our experimental results show that the proposed algorithm is competitive compared to four popular multiobjective evolutionary algorithms.