Using Firefly Algorithm to Solve Resource Constrained Project Scheduling Problem

Sanaei, Pejman; Akbari, Reza; Zeighami, Vahid; Shams, Sheida

doi:10.1007/978-81-322-1038-2_35

Cited by 14 publications

(10 citation statements)

References 28 publications

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“…It is inspired by the flashing lights of fireflies in nature. Previous studies show that the FA can perform superiorly, compared with genetic algorithm and particle swarm optimization [2,3], and it is applicable for mixed variable and engineering optimization [4][5][6][7][8][9][10]. In [11], it presented FA to minimize the real power losses and to improve the voltage profile.…”

Section: Introductionmentioning

confidence: 97%

Enhancing firefly algorithm using generalized opposition-based learning

Zhu²,

et al. 2015

Computing

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Firefly algorithm has been shown to yield good performance for solving various optimization problems. However, under some conditions, FA may converge prematurely and thus may be trapped in local optima due to loss of population diversity. To overcome this defect, inspired by the concept of opposition-based learning, a strategy to increase the performance of firefly algorithm is proposed. The idea is to replace the worst firefly with a new constructed firefly. This new constructed firefly is created by taken some elements from the opposition number of the worst firefly or the position of the brightest firefly. After this operation, the worst firefly is forced to escape from the normal path and can help it to escape from local optima. Experiments on 16 standard benchmark functions show that our method can improve accuracy of the basic firefly algorithm.

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Section: Introductionmentioning

confidence: 97%

Enhancing firefly algorithm using generalized opposition-based learning

Zhu²,

et al. 2015

Computing

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“…In short, 5 years since it is presented, FA has been successfully applied in many areas, such as vector quantization in image compression [41], traveling salesman problem [42], path planning [43,44], PID controller tuning [45], nonlinear optimization problem [46], job shop, Flowshop and project scheduling problems [47][48][49], data clustering [50] and so on. These research demonstrates the high efficiency of FA to solve the NP-hard and combinatorial optimization problem.…”

Section: Introductionmentioning

confidence: 99%

The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

Zhang

Zeng

et al. 2015

Int J Adv Manuf Technol

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This paper proposes a modified discrete firefly algorithm (MDFA) to solve the problem of assembly sequence planning. Firstly, to improve the performance of the firefly algorithm (FA), we proposed a MDFA by endowing the fireflies with the capability of changeable visual range. The computing case shows the proposed algorithm is more effective and robust than standard FA, genetic algorithm and particle swarm optimization algorithm. Secondly, a method of how to set parameters for FA and MDFA is proposed. This method is practical in the application of FA to solve discrete problem. Thirdly, to make the sequences more closer to real industrial requirements, a so called process precedence relations (PPR) evaluation function is presented, which not only considering the interference between parts, assembly tools and clamps, but also regarding the assembly order between parts and their reference parts. Finally, the evolution performance of the MDFA is investigated, and the performance of the proposed approach to solve ASP is verified through two cases study.

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“…A comprehensive study on different types of RCPSP has been presented by Yang et al (2001). In single mode RCPSP, a project consisting of a set of activities with fixed durations and resource requirements is considered (Zeighami et al, 2013). In multi-mode RCPSP, each activity can be executed under different durations with renewable and non-renewable resources (Coelho & Vanhoucke, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Ant colony optimization (Merkle et al, 2002) is another swarm-based optimization algorithm used to solve RCPSP. Firefly algorithm is known as another meta-heuristic methods which has been used by Sanaei et al (2013) for scheduling problems. Generally, different types of meta-heuristics have been used to solve this type of RCPSP.…”

Section: Introductionmentioning

confidence: 99%

A study on the performance of differential search algorithm for single mode resource constrained project scheduling problem

Rahmani¹,

Zeighami²,

Akbari³

2015

10.5267/j.dsl

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Differential Search (DS) algorithm is a new meta-heuristic for solving real-valued numerical optimization. This paper introduces a new method based on DS for solving Resource Constrained Project Scheduling Problem (RCPSP). The RCPSP is aimed to schedule a set of activities at minimal duration subject to precedence constraints and the limited availability of resources. The proposed method is applied to PSPLIB case studies and its performance is evaluated in comparison with some of state of art methods. Experimental results show that the proposed method is effective. Also, it is among the best algorithms for solving RCPSP.Growing Science Ltd. All rights reserved. 5

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Using Firefly Algorithm to Solve Resource Constrained Project Scheduling Problem

Cited by 14 publications

References 28 publications

Enhancing firefly algorithm using generalized opposition-based learning

Enhancing firefly algorithm using generalized opposition-based learning

The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

A study on the performance of differential search algorithm for single mode resource constrained project scheduling problem

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