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Optimization of scheduling problems that involve single or multiple machines with a multimodal objective function and linear or non-linear constraints are generally NP-Hard. The Artificial Bee Colony (ABC) algorithm is a metaheuristic proposed about eighteen years ago and has gained popularity in the swarm intelligence-based evolutionary computation approaches. Various scheduling requirements in diverse domains viz. Electric Vehicle charging, Operating Room, Nurse, Automatic Guided Vehicle (AGV), production, manufacturing, hydrothermal, Printed Circuit Board (PCB), and call centre scheduling are studied. The NP-Hard nature of these scheduling problems render exact methods incompetent for large problem instances thus requiring meta-heuristics such as ABC to produce feasible solutions in a reasonable computation time. The adaptation of ABC in these domains and the outcomes and performance of ABC are highlighted. Based on the case studies, it is found that the requirements of scheduling problems that would benefit from ABC are those that have a planning horizon requiring a near-optimal solution in a reasonably fast time when run on minimal computing capacity. Future works that would merit the application and enhancement of existing ABC techniques are proposed. The case studies presented in this paper motivate R&D Engineers and young researchers to apply ABC techniques of optimization and scheduling in different research topics for achieving energy and cost efficiency. [ABSTRACT FROM AUTHOR]

At present, artificial bee colony algorithm (ABC) is one of the hot issues in swarm intelligence algorithm. Since it was proposed, people have done a lot of improvement work for ABC algorithm. To address the shortcomings of ABC, an improved ABC guided by experience (named as EABC) is proposed in this paper. In EABC, it collects the experience of individual improvement caused by dimension change in the iterative process, and selects the dimensions to change according to a ratio when a new position needs to be generated. In this way, the individual can choose a good direction to improve its quality. Numerical experiments show that EABC has a better performance. [ABSTRACT FROM AUTHOR]