A Vascular Invasive Tumor Growth Optimization Algorithm for Multi-Objective Optimization

Abstract

Multi-objective optimization problems (MOPs) have received much attention in recent years. To deal with these problems, many multi-objective optimization algorithms have been proposed, especially the heuristic algorithms. In this paper, we proposed a multi-objective optimization algorithm called vascular invasive tumor growth optimization (VITGO), which based on the invasive tumor growth optimization and utilized a vascular mechanism to solve the MOPs. The newly proposed algorithm contains two parts: the vascular units and tumor cells. The former ones are utilized to record the Pareto solutions of the MOPs and define the search direction, and the latter ones are utilized to co-operate with vascular units to search deeper and wider. The mechanisms in the VITGO algorithm includes: endpoint generation, approximate Pareto front guidance, opposite searching, and adaptively detailed searching. Experiments showed that compared with other state-of-the-art multi-objective optimization algorithms, VITGO performs better in convergence and diversity.

Keywords

• Evolutionary computation
• swarm intelligence
• multi-objective optimization
• endpoint generation
• vascular mechanism