An improved multi-objective bacteria colony chemotaxis algorithm and convergence analysis

• This paper proposes an improved multi-objective bacteria colony chemotaxis algorithm to solve multi-objective optimization problems.
• Adaptive grid, oriented mutation based on grid and adaptive external archive are introduced to the improved algorithm.
• The Pareto front obtained by the improved algorithm has better distribution and convergence than other optimization algorithms.
• The convergence property of the general Pareto-based multi-objective bacteria colony chemotaxis algorithm is proved.

In this paper, a novel algorithm based on the bacterial colony chemotaxis (BCC) algorithm is developed to solve multi-objective optimization problems. The main objective of the paper is to improve the performance of BCC. Hence, the main work is to add three improvements, which are improved adaptive grid, oriented mutation based on grid and adaptive external archive, in order to improve the convergence performance on multi-objective optimization problems and the distribution of solutions. This paper also presents a first and simple convergence analysis of the general Pareto-based MOBCC. The proposed algorithm is validated using 12 benchmark problems and four performance measures are implemented to compare its performance with the MOBCC algorithm, the NSGA-II algorithm, and the MOEA/D algorithm. The simulation results confirmed the effectiveness of the algorithm.

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