Abstract
The present study integrated a discrete search equation to develop a discrete improved artificial bee colony (DiABC) algorithm. A random binary population was added in the initialization. The current optimal solution was then introduced into the search equations for worker and onlooker bees to effectively enhance the convergence accuracy of the algorithm. And a new selection method on scout bees was proposed. Eighteen benchmark knapsack problems (KPs) experiments were conducted, of which the results validated the effectiveness of the DiABC algorithm in optimizing the KPs.
Highlights
Researchers have been examining 0-1 knapsack problems (KPs) since Dantzig first introduced it in 1957 [1]
We proposed some modifications on the iABC algorithm to improve the convergence performance of the iABC algorithm as well as to enhance the performance of the discrete improved artificial bee colony (DiABC) algorithm
Under the guidance of the current optimal individual search, many individuals approach the optimal solution, which makes the iABC algorithm converge to the global optimal solution with a faster convergence rate
Summary
Researchers have been examining 0-1 knapsack problems (KPs) since Dantzig first introduced it in 1957 [1]. Intelligent algorithms can be applied as suitable solution methods to solve non-differentiable problems with a high number of dimensions. Karaboga [9] take the artificial bee colony (ABC) algorithm to solve many applied optimization problems. Sabet et al [12] presented a binary version of the ABC algorithm for KPs. far, the ABC algorithm is less applied to binary problems, KPs. The present study proposes a new search solution for conditions with the worker and onlooker bees to improve the exploitation based on many of the previously improved ABC algorithms and KP solving methods. We named the newly designed initialization method and new search equations for KPs as ‘‘DiABC’’ for discrete artificial bee colony algorithm, which was developed to solve 0-1 knapsack problems
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