An Adaptive Brain Storm Optimization Algorithm Based on Heuristic Operators for TSP

Abstract

Traveling Salesman Problem (TSP) is a classical combinatorial optimization problem. This paper proposed an MDBSO (Modified Discrete Brain Storm Optimization) algorithm to solve TSP. The convex hull or greedy algorithm was introduced to initialize the population, which can improve the initial population quality. The adaptive inertial selection strategy was proposed to strengthen the global search in the early stage while enhancing the local search in the later stage. The heuristic crossover operator, which is based on the local superior genetic information of the parent to generate new individuals, was designed to improve the search efficiency of the algorithm. The experimental results of different benchmarks show that the MDBSO algorithm proposed in this paper significantly improves the convergence performance in solving TSP.