Abstract
Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population updating strategy, a flexible binary wolf pack algorithm (FWPA) is proposed. Then, FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems, which have numerous practical applications. To the best of our knowledge, this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem. By comparing two state-of-the-art algorithms with the basic BWPA, the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.
Highlights
Most research in evolutionary computation focuses on static problems where the entire problem-related data remains stationary through optimization procedure [1,2,3]
From the comparison of the different versions of flexible binary wolf pack algorithm (FWPA) that own different values of μ, it can be seen that the FWPA with μ = 0:75 performs best, and the performance of the algorithms with μ = 0:5, 1, and 2 is similar to binary Wolf pack algorithm (WPA) (BWPA)
By comparing the results when σ = 0:05 and 0.1, which reflect the severity of the change between two dynamic environments, it can be seen that the differences of two consecutive environments become larger with the increase of σ
Summary
Most research in evolutionary computation focuses on static problems where the entire problem-related data remains stationary through optimization procedure [1,2,3]. Wolf pack algorithm (WPA) [19] is a relatively new and promising member of swarm intelligence-based algorithms that model the cooperative hunting behavior of wolf pack It has been proved an efficient optimizer for solving many nonlinear and complex optimization problems by successful applications in image processing [20], power system control [21], robot path planning [22], and static MKPs [23]. Despite its high efficiency of binary WPA (BWPA) in solving static MKPs, WPA has not been introduced into the area of DMKPs. The key issue of handling DOPs using EAs is how to avoid population diversity loss problem and maintain population diversity while tracking the changing global optima [5, 8, 9, 28].
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