An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

Madani-Isfahani, Mansooreh; Ghobadian, Ehsan; Tekmehdash, Hassan Irani; Tavakkoli–Moghaddam, Reza; Naderi-Beni, Mahdi

doi:10.5267/j.ijiec.2013.02.002

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…In another work (Karimi, Zandieh, & Najafi, 2011), evolutionary imperialist competitive algorithm (ICA), has been used for hybrid flexible flow shop scheduling with sequence-dependent setup times by minimizing maximum completion time. Madani-Isfahani et al (Madani-Isfahani, Ghobadian, Tekmehdash, Tavakkoli-Moghaddam, & Naderi-Beni, 2013) optimizes mean completion times of jobs and mean squares of deviations from machines workload from their averages while using hybrid of ICA and GA.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Criteria Hybrid Grey Wolf Approach for Parallel Machine Job Scheduling

Jeet¹,

Dhir

2017

International Journal of Applied Metaheuristic Computing

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Nature-inspired algorithms are becoming popular due to their ability to solve complex optimization and engineering problems. Grey Wolf algorithm is one of the recent nature-inspired algorithms that have obtained inspiration from leadership hierarchy and hunting mechanisms of grey wolves. In this paper, four formulations of multi-objective grey wolf algorithm have been developed by using combination of weighted objectives, use of secondary storage for managing possible solutions and use of Genetic Algorithm (GA). These formulations are applied for jobs scheduling on parallel machines while taking care of bi-criteria namely maximum tardiness and weighted flow time. It has been empirically verified that GA based multi-objective Grey Wolf algorithms leads to better results as compared to their counterparts. Also the use of combination of secondary storage and GA further improves the resulting schedule. The proposed algorithms are compared to some of the existing algorithms, and empirically found to be better. The results are validated by numerical illustrations and statistical tests.

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Section: Literature Reviewmentioning

confidence: 99%

A Bi-Criteria Hybrid Grey Wolf Approach for Parallel Machine Job Scheduling

Jeet¹,

Dhir

2017

International Journal of Applied Metaheuristic Computing

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“…Until now, ICA has extensively been used to solve different kinds of problems to improve a candidate solution with regard to a given measure of quality. For example: successful implementations of ICA include PID controller design (Atashpaz-Gargari et al, 2008), parameter identification of transformer R-L-C-M model (Rashtchi et al, 2011), optimization in electromagnetics (Coelho et al, 2012), the job shop scheduling problem (Chen et al, 2012), integrated product mix outsourcing (Nazari-Shirkouhi et al, 2010), temperature-dependent functionally graded material (Mozafari et al, 2012), bi-criteria scheduling of the assembly flow shops (Shokrollahpour et al, 2010), and data clustering (Niknam et al, 2011), mixed-model U-line balancing and sequencing problem (Lian et al, 2012), bi-objective unrelated parallel machine scheduling problem (Madani-Isfahani et al, 2013) and the mixed-model assembly line sequencing problem Colonial competitive algorithm (Moradi and Zandieh, 2013). For detailed example: Naimi Sadigh et al (2012) proposes a new assimilation strategy based on normal distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

A modified colonial competitive algorithm for optimizing convex functions

Esmaeilzadeh

2013

International Journal of Intelligent Computing and Cybernetics

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Purpose -This article is going to introduce a modified variant of the imperialist competitive algorithm (ICA). The paper aims to discuss these issues. Design/methodology/approach -ICA is a meta-heuristic algorithm that is introduced based on a socio-politically motivated global search strategy. It is a population-based stochastic algorithm to control more countries. The most powerful countries are imperialists and the weakest countries are colonies. Colonies movement toward their relevant imperialist, and making a competition among all empires to posses the weakest colonies of the weakest empires, form the basis of the ICA. This fact that the imperialists also need to model and they move towards top imperialist state is the most common type of political rules from around the world. This paper exploits these new ideas. The modification is the empire movement toward the superior empire for balancing the exploration and exploitation abilities of the ICA. Findings -The algorithms are used for optimization that have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. MICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing some modification on it. Originality/value -Therefore, several solution procedures, including ICA, modified ICA, and genetic algorithm and particle swarm optimization algorithm are proposed. Finally, numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures. Test results present the suitability of the proposed modified ICA for convex functions with little fluctuations.

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“…Compared with GA and PSO, the ICA performs better in the experiment and the quality of result is less related to the initial populations. Moreover, Madani-Isfahani et al [21] presented an ICA to solve a biobjective unrelated parallel machine scheduling problem where setup times are sequence dependent.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

Zhang

et al. 2017

Mathematical Problems in Engineering

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Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

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An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

Cited by 7 publications

References 27 publications

A Bi-Criteria Hybrid Grey Wolf Approach for Parallel Machine Job Scheduling

A Bi-Criteria Hybrid Grey Wolf Approach for Parallel Machine Job Scheduling

A modified colonial competitive algorithm for optimizing convex functions

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

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