A Many-Objective Artificial Bee Colony Algorithm Based on Adaptive Grid

Abstract

Many-objective optimization problems are widely applied and complex to solve. For most many-objective evolutionary algorithms, maintaining the balance of solution convergence and diversity is a challenging problem. Considering the convergence and diversity at the same time, a many-objective artificial bee colony algorithm based on adaptive grid(MOABCAG) is proposed. By searching for the ideal target point in the grid, the solution selection pressure is enhanced, the solution is positioned under the designed grid, and the grid is adaptively divided to improve the convergence and diversity of the solution. The algorithm improves the position sharing mechanism of the leader and follower bees in the artificial bee colony algorithm, and it sets the variable neighborhood search method of the follower bee to improve the precision of the solution vectors. MOABCAG is compared with five well-known evolutionary algorithms on thirteen benchmark test functions. The results show that the proposed MOABCAG algorithm obtains better performance than other related state-of-the-art algorithms in solving such many-objective optimization problems.

Keywords

• Many-objective optimization
• adaptive grid
• artificial bee colony algorithm