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.
Highlights
Multi-objective optimization problems (MOPs) are decision problems with multiple criteria
We introduced the vascular mechanism into ITGO and proposed a multi-objective vascular invasive tumor growth optimization, which both imitates the behaviour of vascular units and tumor cells to solve multi-objective optimization problems
MULTI-OBJECTIVE INVASIVE TUMOR GROWTH OPTIMIZATION In this paper, we proposed a vascular invasive tumor growth optimization algorithm VITGO, which imitates the behaviours of vascular units and tumor cells to search for a significant Pareto front of a MOP
Summary
Multi-objective optimization problems (MOPs) are decision problems with multiple criteria. The invasive tumor growth optimization algorithm/ ITGO [4] is an algorithm that imitates the behaviour of tumor cells to solve single-objective optimization problems by searching out their global best solutions. 2) Specific proposal of a series of search schemes to solve multi-objective optimization problems. Zhou et al.: VITGO Algorithm for Multi-Objective Optimization discrete or discontinuous parts of the approximate Pareto front in the intermediate results The endpoints of these discrete or discontinuous structure are more likely to help search wider and farther. Ii) If an individual reaches the boundary, it will random walk with the corresponding dimension that exceeds the boundary and other variables remain unchanged These search schemes can both help for the convergence and diversity of the VITGO algorithm.
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