Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
INFORMS Journal on Computing
A Heuristic Method for the Set Covering Problem
Operations Research
Performance of Various Computers Using Standard Linear Equations Software
Performance of Various Computers Using Standard Linear Equations Software
A new adaptive multi-start technique for combinatorial global optimizations
Operations Research Letters
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Competitive Hopfield network combined with estimation of distribution for maximum diversity problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
New ideas for applying ant colony optimization to the set covering problem
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Seeking global edges for traveling salesman problem in multi-start search
Journal of Global Optimization
Survey: Covering problems in facility location: A review
Computers and Industrial Engineering
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The construction of good starting solutions for multi-start local search heuristics is an important, yet not well-studied problem. In these heuristics, randomization methods are usually applied to explore new promising areas and memory mechanisms are incorporated with the main purpose of reinforcing good solutions. Under the template of a typical multi-start metaheuristic, Meta-RaPS (Meta-heuristic for Randomized Priority Search), this paper presents several randomization methods and memory mechanisms with a focus on comparing their effectiveness and analyzing their interaction effects. With the Set Covering Problem (SCP) as the application problem, it is found that these randomization methods work well for Meta-RaPS with an improvement phase while the memory mechanisms better the solution quality of the construction phase. The quality and efficiency of Meta-RaPS can be improved through the use of both memory mechanisms and randomization methods. This paper also discovers several efficient algorithms that maintain a good balance between randomness and memory and finds the optimal or best-known solutions for the 65 SCP test instances from the OR-library.