A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem

Zouache, Djaafar; Moussaoui, Abdelouahab; Abdelaziz, Fouad Ben

doi:10.1016/j.ejor.2017.06.058

Cited by 57 publications

(19 citation statements)

References 37 publications

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“…3. A resource allocation algorithm in view of a multiobjective cooperative swarm intelligence algorithm, 32 centralizing to take care of the optimization problem by utilizing the FFA and PSO, is utilized. Figure 1 demonstrates the system model of an UL resource allocation in the LTE-A network.…”

Section: Proposed Methodsmentioning

confidence: 99%

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Moses¹,

Balakrishnan²

2019

Trans Emerging Tel Tech

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Nowadays, Long-Term Evolution Advanced (LTE-A) network is the primary innovation in 4G networks. The LTE-A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE-A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0-1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0-1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)-based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE-A network than state-of-art methods in the proposed strategy.Trans Emerging Tel Tech. 2019;30:e3748.wileyonlinelibrary.com/journal/ett

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Section: Proposed Methodsmentioning

confidence: 99%

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Moses¹,

Balakrishnan²

2019

Trans Emerging Tel Tech

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show abstract

“…where i and j are solution indexes. The spacing metric evaluates the uniformity of the distribution of non-dominated solutions [40], [41]: where…”

Section: ) Algorithm Processmentioning

confidence: 99%

A Layout Generation Algorithm for Unequal-Area Display and Control Console Considering Ergonomics

2020

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This study addresses the problem of designing a display and control console layout under ergonomic principles via a mathematical approach. An integer programming model is established with objective functions comprising four ergonomic principles for designing the display and control console. Unlike the traditional design methods that consider equal-area devices, the new method addresses the general ergonomic console layout problem with unequal-area components. The presented approach evaluates the console in its entirety. Due to the NP-hardness of the problem, a modified multiobjective particle swarm algorithm is proposed. Compared with the manually layout console of the electric locomotive HXD3D, our proposed model achieves better results. The layout of most of the devices can embody ergonomic principles. It is convenient for designers that the proposed method can effectively produce many good layout schemes and evaluate the layout from an ergonomic perspective.

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“…To obtain the coefficient vector λ λ λ k , PSORisk simply rounds the position values to the nearest integer numbers. Although there are a number of techniques proposed in the literature [29] for discrete solutions, we choose the rounding technique because of their simplicity and straightforward implementation. A feature is considered as selected if its rounded coefficient is not zero.…”

Section: B Psorisk Representation and System Overviewmentioning

confidence: 99%

Swarm-Based Machine Learning Algorithm for Building Interpretable Classifiers

et al. 2020

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A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem

Cited by 57 publications

References 37 publications

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

A Layout Generation Algorithm for Unequal-Area Display and Control Console Considering Ergonomics

Swarm-Based Machine Learning Algorithm for Building Interpretable Classifiers

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