An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

The paper presented an optimization scheduling problem in complex conditions. A genetic algorithm and tabu search hybrid algorithm (GATS) was designed to solve this problem. The algorithm used the global optimization capacity of genetic algorithm and the local hill climbing advantage of tabu search in the search process. The principium of the algorithm was introduced and a contrast experiment was carried out. The experiment and the analysis indicate the validity of the GATS to the optimization scheduling problem in complex conditions.

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Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization

Applied Intelligence ◽

10.1007/s10489-021-02776-7 ◽

2021 ◽

Author(s):

Zongshan Wang ◽

Hongwei Ding ◽

Zhijun Yang ◽

Bo Li ◽

Zheng Guan ◽

...

Keyword(s):

Global Optimization ◽

Salp Swarm Algorithm ◽

Opposition Based Learning ◽

Swarm Algorithm

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An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021352 ◽

2021 ◽

Vol 18 (6) ◽

pp. 7076-7109

Author(s):

Shuang Wang ◽

◽

Heming Jia ◽

Qingxin Liu ◽

Rong Zheng ◽

...

Keyword(s):

Global Optimization ◽

Heuristic Algorithms ◽

Optimization Problems ◽

Search Space ◽

Search Performance ◽

Exploration And Exploitation ◽

Design Problems ◽

Local Optima ◽

Opposition Based Learning ◽

Engineering Design Problems

<abstract> <p>This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.</p> </abstract>

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An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies

Computational Intelligence and Neuroscience ◽

10.1155/2019/1589303 ◽

2019 ◽

Vol 2019 ◽

pp. 1-24 ◽

Cited By ~ 10

Author(s):

Tongyi Zheng ◽

Weili Luo

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Learning Strategy ◽

State Of The Art ◽

Metaheuristic Algorithms ◽

Competitive Performance ◽

Exploration And Exploitation ◽

Global Optimization Algorithm ◽

Opposition Based Learning ◽

Searching Ability

Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.

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