Xiangjing Lai

TL;DR: The proposed algorithm integrates two solution-based tabu search methods into the evolutionary framework that applies a hyperplane-constrained crossover operator to generate offspring solutions, a dynamic method to determine search zones of interest, and a diversity-based population updating rule to maintain a healthy population.

TL;DR: This work proposes a two-stage search algorithm, where the first stage aims to locate a promising hyperplane within the whole search space and the second stage tries to find improved solutions by exploring the reduced subspace defined by the hyperplane.

TL;DR: This paper investigates a novel quantum particle swarm optimization algorithm, which integrates a distanced-based diversity-preserving strategy for population management and a local optimization method based on variable neighborhood descent for solution improvement.

TL;DR: This work demonstrates that examining both feasible and infeasible solutions during the search is a highly effective search strategy for the considered coloring problem and could beneficially be applied to other constrained problems as well.

TL;DR: An effective solution-based tabu search (SBTS) algorithm for solving the Max-Minsum DP approximately is introduced, characterized by the joint use of hash functions to determine the tabu status of candidate solutions and a parametric constrained swap neighborhood to enhance computational efficiency.