A New Evolutionary Multiobjective Model for Traveling Salesman Problem

Abstract

The traveling salesman problem (TSP) is one of the most classical NP-hard problems in the combinatorial optimization, as many practical problems, such as scheduling problems and vehicle-routing cost allocation problems can be abstracted. The introduction of multiobjective in the TSP is a very important research topic, which brings serious challenges to the TSP. Currently, genetic algorithms (GAs) are one of the most effective methods to solve the multiobjective traveling salesman problem (MOTSP). However, GA-based algorithms suffer the premature convergence, the insufficient diversity, and nonuniform distribution of solutions when solving the MOTSP, which further restrict the wide application of GA-based algorithms. In order to overcome these problems, this paper proposes an improved method for GAs based on a novel evolutionary computational model, named the Physarum-inspired computational model (PCM). Based on the prior knowledge of the PCM, the initialization of the population in the proposed method is first optimized to enhance the distribution of solutions. Then, the hill climbing (HC) method is used to improve the diversity of individuals and avert running into the local optimum. Compared to the other MOTSP solving algorithms, a series of experimental results demonstrate that our proposed method achieves a better performance. INDEX TERMS Bi-objective traveling salesman problem, NSGA-II, hill climbing, Physarum.

Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing

“…Several types of research work also developed nature-inspired models for MOO. For instance, an improved method of GA based on an evolutionary computational model, namely the Physarum-Inspired Computational Model (PCM), was proposed in [ 26 ]. The initialization of the population used prior knowledge of PCM.…”

Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

“…The exploitation phase starts when FDA has reached the maximum depth k. The Intensive Local Search (ILS) starts to explore intensively the sub-hyperspheres. ILS starts at the center of each sub-hypersphere and moves along each dimension sequentially, evaluating two solutions x s1 and x s2 as expressed in (2) and (3), respectively.…”

“…Most of the well-known metaheuristics (e.g. evolutionary algorithms, particle swarm, ant colonies) have been adapted to solve multi-objective problems [2], [3], [13], [30], [31]. For instance, authors in [12] have proposed a new Particle Swarm Optimization algorithm to solve multi-objective problems based on double-archive mechanisme and Levy Flight.…”

Dynamic Community Detection Based on a Label-Based Swarm Intelligence

“…Nowadays, the evolutionary-based multi-objective optimization algorithms have made great progress. It has been proved that evolutionary algorithms can solve multi-objective optimization problems efficiently [41], [42]. The particle swarm algorithm, a typical swarm intelligence method, is much better than other algorithms in the community discovery [23].…”