An Improved Bacteria Foraging Optimization Algorithm for High Dimensional Multi-objective Optimization Problems

In this paper, an improved bacterial foraging optimization algorithm (BFO), which is inspired by the foraging and chemotactic phenomenon of bacteria, named high dimensional multi-objective bacterial foraging optimization (HMBFO) is introduced for solving high dimensional multi-objective optimization (MO) problems. The high-dimension update strategy is presented in this paper to solve the problem that the global Pareto solutions can be hardly obtained by traditional MBFO in high-dimension MO problems. According to this strategy, the position of bacteria not only can be rapidly updated to the optimal solution, but also can enhance the searching precision and reduce chemotaxis dependency remarkably. Moreover, the penalty mechanism is considered for solving the inequality constraints MO problems, and three different performance metrics (Hypervolume, Convergence metric, Spacing metric) are introduced to evaluate the performances of algorithms. Compared with the other four evolutionary MO algorithms (MBFO, MOCLPSO, MOPSO, PESA2), the simulation result shows that in most cases, the proposed algorithm carries out better than the other existing algorithms, it has high efficiency, rapid speed of convergence and strong search capability of global Pareto solutions.