MOICA: A novel multi-objective approach based on imperialist competitive algorithm

Enayatifar, Rasul; Yousefi, Moslem; Abdullah, Abdul Hanan; Darus, Amer Nordin

doi:10.1016/j.amc.2013.03.099

Cited by 49 publications

(25 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, using the multi-objective imperialist competition algorithm [14], suitable values for power and wave length of pumps are found so that the value of gain ripple and the total using power of r pumps in S-band with bandwidth of 80nm are both minimized at the same time.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Performance Optimization of the Multi-Pumped Raman Optical Amplifier using MOICA

Jahromi¹,

Saif²,

Jabbari³

2016

ijacsa

View full text Add to dashboard Cite

Abstract-In order to achieve the best gain profile for multi pump distributed Raman amplifiers in Wavelength Division Multiplexing (WDM) transmission systems, the power and wavelength of pumps, the type of pumping configuration and the number of pump signals are the most important factors. In this paper, using a Multi-Objective Imperialist Competition Optimization Algorithm (MOICA) with lowest power consumption and lowest number of pumps, we propose the most uniform gain profile for two types of pumping configurations in S-band and compare the results. Considering the design conditions including the type of pumping configuration, fiber length, fiber type and number of pump signals and using the multi-objective algorithm, we propose a method which can be used to achieve a gain level in which the amplifier has the lowest power consumption and lowest gain ripple. According to this, we can design a powerful WDM transmission system by Distributed Raman Amplifier (DRA) with a good performance and efficiency.

show abstract

Section: Simulation and Resultsmentioning

confidence: 99%

Performance Optimization of the Multi-Pumped Raman Optical Amplifier using MOICA

Jahromi¹,

Saif²,

Jabbari³

2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…Its capability in finding a near optimal solution for different single objective, leads to use this approach for multi-objective problems. Enayatifar et al [32] developed an EV based on ICA for a multi-objective problem, called multi-objective imperialist competitive algorithm (MOICA) and tested this algorithm on six well-known test functions.…”

Section: Multi-objective Imperialist Competitive Algorithm (Moica)mentioning

confidence: 99%

A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms

Fattahi

Hajipour

Nobari

2015

Applied Soft Computing

View full text Add to dashboard Cite

“…[48][49][50] Among the metaheuristic approaches, the imperialist competitive algorithm (ICA) is a population-based algorithm that was presented by Atashpaz-Gargari and Lucas 51 So far, the ICA-based approaches have been applied on different multiobjective optimization problems that were aimed at providing a set of Pareto optimal solutions, such as assembly line design 52 , coupling of photovoltaicelectrolyzer system 53 , sensor deployment in wireless sensor networks 54 , and benchmarked functions. 55,56 Hosseini and Al Khaled 57 have presented a state-of-theart literature survey on the application of ICA in different engineering disciplines. In accordance with the reviewed studies, MOICA has been found to be more efficient than the other approaches for solving engineering problems of different domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

Piroozfard¹,

Wong²,

Tiwari³

2018

IJCIS

View full text Add to dashboard Cite

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches.

show abstract

MOICA: A novel multi-objective approach based on imperialist competitive algorithm

Cited by 49 publications

References 22 publications

Performance Optimization of the Multi-Pumped Raman Optical Amplifier using MOICA

Performance Optimization of the Multi-Pumped Raman Optical Amplifier using MOICA

A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

Contact Info

Product

Resources

About