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Jianjun Jiao

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2 papers

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EAAI Journal 2020 Journal Article

Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model

Wen Long
Tiebin Wu
Jianjun Jiao
Mingzhu Tang
Ming Xu

Whale optimization algorithm (WOA) is a relatively new meta-heuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving high-dimensional 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 refraction-learning 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 state-of-the-art meta-heuristic algorithms for solving high-dimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.

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EAAI Journal 2018 Journal Article

An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization

Wen Long
Jianjun Jiao
Ximing Liang
Mingzhu Tang

Grey wolf optimizer (GWO) algorithm is a relatively novel population-based optimization technique that has the advantage of less control parameters, strong global optimization ability and easy of implementation. It has received significant interest from researchers in different fields. However, there is still an insufficiency in the GWO algorithm regarding its position-updated equation, which is good at exploitation but poor at exploration. In this work, we proposed an improved algorithm called the exploration-enhanced GWO (EEGWO) algorithm. In order to improve the exploration, a new position-updated equation is presented by applying a random individual in the population to guide the search of new candidate individuals. In addition, in order to make full use of and balance the exploration and exploitation of the GWO algorithm, we introduced a nonlinear control parameter strategy, i. e. , the control parameter of a → is nonlinearly increased over the course of iterations. The experimental result on a set of 23 benchmark functions and 4 engineering applications demonstrate the effectiveness and efficiency of the modified position-updated equation and the nonlinear control parameter strategy. The comparisons show that the proposed EEGWO algorithm significantly improves the performance of GWO. Moreover, EEGWO offers the highest solution quality, strongest robustness, and fastest global convergence among all of the contenders on almost all of the test functions.

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