Thainiam, Pimprapai

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Thainiam, Pimprapai
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Thainiam, P.
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pimprapai.th@kmitl.ac.th

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    Developing tabu search with intensification and diversification for the seriation problem
    (2018-06-15)
    Thainiam, Pimprapai
    The seriation problem is an important problem in combinatorial optimization. The goal of seriation is to find a linear order for data objects to reveal structural information given a loss or a merit objective function as an objective function. For the seriation problem, finding an optimal solution using exact algorithms (e.g., branch-and-bound) to find the optimal solution is currently impractical for problems with more than 35 objects. In this paper, we develop a new heuristic procedure to maximize the gradient measure which is our selected merit objective function. The proposed heuristic incorporates search intensification and diversification into standard tabu search (TS) algorithm. From our experimental results, it shows that intensification and diversification tabu search (IDTS) outperforms the standard TS algorithms in terms of efficiency, effectiveness, and robustness.
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    The Effects of Memes on Memetic Algorithms for Solving Quadratic Assignment Problem
    (2019-12-01)
    Thainiam, P.
    The quadratic assignment problem (QAP) is known as one of NP-hard combinatorial optimization problems where a set of facilities must be assigned to a set of locations in order to minimize total cost. In this paper, we present the effect of local search algorithm referred to as meme on Memetic Algorithms (MAs). We also compare four different local search metaheuristics: Hill Climbing Algorithm (HC), Tabu Search (TS), Simulated Annealing (SA), and Iterated Local Search (ILS) for solving QAP and analyze their performance in terms of solution quality. The results show that ILS is the best metaheuristic followed by SA, TS, and HC, respectively. While the MA using ILS as a meme is the best among all four MAs, the MA using SA as a meme is not the second-best metaheuristic, but the worst among all.