Modified global best artificial bee colony for constrained optimization problems. (April 2018)

• Record Type: Journal Article
• Authors: Bansal, Jagdish Chand; Joshi, Susheel Kumar; Sharma, Harish
• Is Part Of: Computers & electrical engineering. Volume 67(2018)
• Page Start: 365
• Page End: 382
• Publication Date: 2018-04
• Subjects: Artificial bee colony -- Constrained optimization -- Optimal power flow problem -- Exploration -- Exploitation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.10.021
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Gbest guided artificial bee colony algorithm is modified for constrained optimization problems. The Modified ABC is named as Modified Gbest ABC (MGABC). Apart from the test problems, the MGABC is tested over 3 engineering design problems. The proposed MGABC is also applied to solve the optimal power flow problem from electrical engineering. Based on the comparison of the numerical results with that of earlier approaches, MGABC is recommended a better optimizer. Abstract: Artificial Bee Colony (ABC) is one of the most popular nature inspired optimization algorithms. Recently, a variant of ABC, Gbest-guided ABC (GABC) was proposed. GABC was verified to perform better than ABC, in terms of efficiency and reliability. In the position update process of GABC, Gbest (the best individual in the swarm) individual influences the movement of the swarm. This movement may create a cluster around the Gbest individual which further leads to the premature convergence, particularly for constrained optimization problems. This paper presents a modification in GABC for constrained optimization problems. GABC is modified in both employed and onlooker bee phases by incorporating the concept of fitness probability based individual movement. The modified GABC is tested over 20 constrained benchmark problems and applied to solve 3 engineering design problems. Optimal power flow problem has also been solved using modified GABC to check the efficiency of the proposed algorithm.