Abstract
A probabilistic local search algorithm called simulated annealing ( SA) is a useful approximate solution technique for multi-objective optimization problems. When we use SA to solve multi-objective optimization problems, we cannot use an acceptance probability function used for single objective optimization problems. Therefore, several types of acceptance probability functions for multi-objective SA have been previously proposed. In this paper, we introduce a parameterized acceptance probability function for multi-objective SA, which changes its type depending on the parameter, and investigate how the performance of the multi-objective SA depends on the type of acceptance probability function in two test problems. Scope and purpose There are many real-life problems that are formulated as multi-objective optimization problems. However, large-scale problems of this type are often difficult to solve by specific conventional exact procedures. Simulated annealing ( SA) is an efficient tool for finding useful approximate solutions to such problems. Several types of acceptance probability functions are available for multi-objective SA. In this paper, we investigate how the performance of the multi-objective SA depends on the type of acceptance probability function applied.
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