Cooperative Artificial Bee Colony Algorithm With Multiple Populations for Interval Multiobjective Optimization Problems

2019 ◽  
Vol 27 (5) ◽  
pp. 1052-1065 ◽  
Author(s):  
Liming Zhang ◽  
Saisai Wang ◽  
Kai Zhang ◽  
Xiuqing Zhang ◽  
Zhixue Sun ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Multiple Populations ◽  
Bee Colony ◽  
Multiobjective Optimization Problems

scholarly journals An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework

2018 ◽  
Vol 2018 ◽  
pp. 1-26
Author(s):  
Jiuyuan Huo ◽  
Liqun Liu
Keyword(s):  
Multiobjective Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Resource Planning ◽  
Planning Problem ◽  
Abc Algorithm ◽  
Optimization Framework ◽  
Bee Colony ◽  
Multiobjective Optimization Problems ◽  
Evolution Algorithms

The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been integrated into the common multiobjective optimization frameworks which provide the integrated environments for understanding, reusing, implementation, and comparison of multiobjective algorithms. Therefore, a unified, flexible, configurable, and user-friendly MOABC algorithm framework is presented which combines a multiobjective ABC algorithm named RMOABC and the multiobjective evolution algorithms (MOEA) framework in this paper. The multiobjective optimization framework aims at the development, experimentation, and study of metaheuristics for solving multiobjective optimization problems. The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application. The experiment’s results showed the framework can deal with practical multiobjective optimization problems more effectively and flexibly, can provide comprehensive and reliable parameters sets, and can complete reference, comparison, and analysis tasks among multiple optimization algorithms.

scholarly journals A Modification Artificial Bee Colony Algorithm for Optimization Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jun-Hao Liang ◽  
Ching-Hung Lee
Keyword(s):  
Mobile Robot ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Computational Effort ◽  
Function Optimization ◽  
Cooperative Strategy ◽  
Robot System ◽  
Bee Colony ◽  
And Performance

This paper presents a modified artificial bee colony algorithm (MABC) for solving function optimization problems and control of mobile robot system. Several strategies are adopted to enhance the performance and reduce the computational effort of traditional artificial bee colony algorithm, such as elite, solution sharing, instant update, cooperative strategy, and population manager. The elite individuals are selected as onlooker bees for preserving good evolution, and, then, onlooker bees, employed bees, and scout bees are operated. The solution sharing strategy provides a proper direction for searching, and the instant update strategy provides the newest information for other individuals; the cooperative strategy improves the performance for high-dimensional problems. In addition, the population manager is proposed to adjust population size adaptively according to the evolution situation. Finally, simulation results for optimization of test functions and tracking control of mobile robot system are introduced to show the effectiveness and performance of the proposed approach.

scholarly journals Artificial Bee Colony Algorithm with Directed Scout

2021 ◽  
Author(s):  
Radhwan A.A. Saleh ◽  
Rüştü Akay
Keyword(s):  
Design Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Real Life ◽  
Search Space ◽  
Bee Colony ◽  
Life Problems ◽  
Tension And Compression ◽  
Teaching Learning

Abstract As a relatively new model, the Artificial Bee Colony Algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modify the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on the CEC2014 benchmark suite, CEC2015 benchmark functions, and three real-life problems: pressure vessel design problem, tension and compression spring design problem, and Frequency-Modulated (FM) problem. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results.

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