M. Yaghini, N. Ghazanfari,
Volume 21, Issue 2 (5-2010)
Abstract
The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the optimization property of tabu search and the local search capability of k-means algorithm together. The contribution of proposed algorithm is to produce tabu space for escaping from the trap of local optima and finding better solutions effectively. The Tabu-KM algorithm is tested on several simulated and standard datasets and its performance is compared with k-means, simulated annealing, tabu search, genetic algorithm, and ant colony optimization algorithms. The experimental results on simulated and standard test problems denote the robustness and efficiency of the algorithm and confirm that the proposed method is a suitable choice for solving data clustering problems.
Abdolreza Roshani, Davide Giglio,
Volume 31, Issue 2 (6-2020)
Abstract
Multi-manned assembly line balancing problems (MALBPs) can be usually found in plants producing large-sized high-volume products such as automobiles and trucks. In this paper, a cost-oriented version of MALBPs, namely, CMALBP, is addressed. This class of problems may arise in final assembly lines of products in which the manufacturing process is very labor-intensive. Since CMALBP is NP-Hard, a heuristic approach based on a tabu search algorithm is developed to solve the problem. The proposed algorithm uses two neighborhood generation mechanisms, namely swap and mutation, that effectively collaborate with each other to build new feasible solutions; moreover, two separate tabu lists (associated with the two generation mechanisms) are used to check if moving to a new generated neighbor solution is forbidden or allowed. To examine the efficiency of the proposed algorithm, some experimental instances are collected from the literature and solved. The obtained results show the effectiveness of the proposed tabu search approach.
