Abstract— In this paper we have tried to develop an altered version of the artificial bee colony algorithm which is inspired from and combined with the meta-heuristic algorithm of firefly. In this method, we have tried to change the main equation of searching within the original ABC algorithm. On this basis, a new combined equation was used for steps of employed bees and onlooker bees. For this...

This paper proposes a hybrid artificial bee colony (HABC) to solve the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP). HABC algorithm uses two-layer coding method establish initial population as nectar source for employed bees. In optimization process, phase and onlooker adopt improved crossover mutation strategies adaptive neighborhood search generate new sources, gree...

The unrelated parallel machine scheduling problem (UPMSP) is a typical production with certain symmetries on machines. Additional resources and preventive maintenance (PM) extensively exist machines; however, UPMSP additional PM has been scarcely investigated. Adaptive competition also seldom implemented in the artificial bee colony algorithm for scheduling. In this study, investigated, which A...

Inspired by the intelligent foraging behavior of honey bees, the artificial bee colony algorithm (ABC), a swarm-based stochastic optimization method, has shown to be very effective and efficient for solving optimization problems. However, since its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. To better balance the tra...

For solving non-linear programming problems containing discrete and continuous variables, this article suggests two modified algorithms based on differential evolution (DE). The two proposed algorithms incorporate a novel random search strategy into DE/best/1 and DE/cur-to-best/1 respectively. Inspired by the artificial bee colony algorithm, the random search strategy overcomes the searching un...

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC has slow convergence rate when it used solve complex optimization problems since solution search equation more of an exploration than exploitation operator. This paper presents improved for solving integer programming minimax problems. pro...

Traditionally, process planning and scheduling were performed sequentially, where depended on the result of planning. Considering their complementarity, two functions are more tightly integrated to improve performance job shop flexible manufacturing environment. This study proposes an adaptive multi-strategy artificial bee colony (AMSABC) algorithm solve (IPPS) problem. In AMSABC, search strate...

Classification of data in large repositories requires efficient techniques for analysis since a large amount of features is created for better representation of such images. Optimization methods can be used in the process of feature selection to determine the most relevant subset of features from the data set while maintaining adequate accuracy rate represented by the original set of features. ...

Aiming at the problem of multisensor resource scheduling in missile early warning operation, a decomposition strategy for tasks under cooperative detection is proposed. Taking benefit factor, target threat and handover factor as fitness function, we establish sensor-subtask assignment (SSA) model propose hybrid discrete artificial bee colony (HDABC) algorithm to solve optimal solution SSA model...