A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem

Dai

Mathematical Problems in Engineering

et al. 2017

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS).

Section: Related Workmentioning

confidence: 99%

“…It must be noted that the item called habitat in the BBO algorithm is similar to that called chromosome in GA. Accordingly, we extended the representation employed in [32] to form a three-vector string. For a clear and intuitive understanding, we present Table 1 as a simple example to describe the encoding and decoding scheme.…”

Section: Encoding and Decodingmentioning

confidence: 99%

A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

Dai

Mathematical Problems in Engineering

et al. 2017

Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

“…Biogeography-based optimization is a new evolutionary optimization algorithm for global optimization which can be very useful in many applications [9][10][11]. The habitats in BBO algorithm are individual units, each habitat represents a solution to the problem.…”

Section: (Shannon) Entropy Criterion Based Measurementioning

confidence: 99%

An Improved Image Segmentation Method based on Shannon Entropy and Biogeography based Optimization

Feng¹

2017

Abstract. For the purpose of improve the effect of multilevel thresholding image segmentation, a new evolutionary optimization algorithm based on the science of biogeography for global optimization has been bring in namely Biogeography based optimization (BBO). In this paper we propose an improvement to BBO. In order to improve the diversity of population and to enhance its exploration ability, the Gaussian mutation operator is integrated into biogeography based optimization (BBO). And we combine this improved evolutionary algorithm and Shannon entropy to get multilevel thresholds of image segmentation. Experiments have been conducted on several images and compared with other algorithm namely ABC and DE.Simulation results and comparisons demonstrate the proposed BBO algorithm is better in terms of the quality of the solutions obtained.

“…These are speciation (the determine performance of new animals), the extinction of animals and the migration of animals between islands. Despite being the recent optimization algorithm, the BBO has been used in many optimization problems such as: economic dispatch solution [2], power flow problem [3], cognitive radio systems [4], security audit trail analysis [5], satellite remote sensing images [6], AC transmission system devices [7], approach for segmentation of human head [8], profit maximization of a generation company [9], flexible job shop scheduling problem [10], mirrored traveling tournament problem [11]. However, there is no major study about application of BBO for optimum design of RC structures.…”

Section: Introductionmentioning

confidence: 99%

Comparison of metaheuristics on multi objective (Cost-C02) optimization of RC cantilever retaining walls

Aydoğdu¹

2017

Pamukkale J Eng Sci