Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks

Potti,

doi:10.3844/jcssp.2011.17.26

Cited by 10 publications

(5 citation statements)

References 20 publications

(16 reference statements)

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“…Its widespread application spans across academic and industrial domains for various multi-objective design purposes [48]. According to Jain et al [49], [50], [51] SPEA2 has shown better performance for multi-objective optimization problems than other algorithms such as Non-dominated Sorting Genetic Algorithm II (NSGAII), Multi-Objective Particle Swarm Optimization (MOPSO), Pareto envelop-based selection algorithm (PESA2).…”

Section: Multi-objective Grey Wolf Optimization (Mgwo) Algorithmmentioning

confidence: 99%

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

Hussain,

Salman Khalid,

Alkahtani

et al. 2024

IEEE Access

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Traditionally, projects have been scheduled with a primary focus on optimizing economic benefits, often at the expense of environmental considerations. However, in today's context, there is an increasing societal demand for organizations to incorporate sustainability practices into their project management policies. This research aims to address this pressing issue by developing a comprehensive mathematical model that takes into account two crucial objectives: project net present value (NPV) and greenhouse gas (GHG) emissions. This model introduces carbon emission constraints on a per-period basis, alongside the usual precedence and resource constraints. the problem represented by this model is proven to be NP-hard, which adds complexity to its solution. To tackle this challenge, two multi-objective metaheuristic algorithms, specifically the Modified Grey Wolf Optimizer (MGWO) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), have been employed to solve the model. Extensive comparative analyses of these me-ta heuristics across various multi-objective performance measures indicate that MGWO consistently outperforms SPEA2 in most scenarios. To illustrate the practical applicability of the pro-posed model, a real-world case study has been utilized. The results of this case study offer compelling insights, showing that project NPV can be significantly improved with only a marginal increase in GHG emissions. This finding highlights the model's potential to provide pragmatic solutions and valuable trade-off options for project planners and practitioners striving to balance economic objectives with environmental sustainability.

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Section: Multi-objective Grey Wolf Optimization (Mgwo) Algorithmmentioning

confidence: 99%

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

Hussain,

Salman Khalid,

Alkahtani

et al. 2024

IEEE Access

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“…We briefly review routing algorithms that purpose of these routing algorithms is to schedule the messages in different independent subsets in order to avoid the path conflicts in the network (Shanmugam et al, 2011;Potti and Chinnasamy, 2011). Five algorithms of routing algorithms that solve the crosstalk problem in MIN are four heuristic algorithms, Genetic Algorithm (GA), Simulated Annealing algorithm (SA), Zero algorithm and Ant Colony Optimization algorithm (ACO).…”

Section: Routing Algorithmsmentioning

confidence: 99%

A Challenge for Routing Algorithms in Optical Multistage Interconnection Networks

Moudi¹,

Othman²

2011

Journal of Computer Science

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Problem statement: A class of dynamic interconnection networks is Multistage Interconnection Networks (MINs) that connects input devices to output devices through a number of switch stages. MINs have assumed importance in recent years; because of their cost-effectiveness. Optical MINs are one type of MINs that have large transmission capacity in the communication networks. There is a major problem in Optical MIN that is crosstalk, which is caused by coupling two signals within a switching element. Approach: To avoid crosstalk in Optical MINs many algorithms have been proposed by many researchers that we review applying five routing algorithms and scheduling them in the Optical MINs. Results: The comparative results of routing algorithms show affective of avoiding crosstalk in number of passes and execution time for different algorithm. Conclusion: The challenge between these routing algorithms is thoroughly investigated, by applying them on Optical MIN and showing which algorithm has better performance to avoid crosstalk

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“…Single objectives approach will find the best single solution and combine multiple objective functions into one single objective function [5].Multiple objectives optimization will find non-dominated/pareto-optimal solutions from its process [6].When we say a pareto-optimal is when the solution cannot increase its benefits from one aspect without sacrificing another aspect [7]. Each solution cannot be considered better than the other by considering its objectives [8]. This research will continue the last research conducted by Hidayatiand Wibowo [9] which solved the multi objectives optimization problem using by assigning weight coefficient to each objective and combine it into one single objective using Native BPSO and Novel BPSO [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Physician Scheduling using Non-Dominated Sorting Native Binary Particle Swarm Optimization with Deallocation Method

Aristya¹

2020

IJETER

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Scheduling Optimization is a critical operation in Hospital Management. A good medical personnel schedule can overcome a lot of troubles for operational staff, management and the medical personnel. This problem can be solved using single objectives optimization and multi objectives optimization. In previous research, the scheduling optimization problem was solved single-objectively using Native Binary Particle Swarming Optimization (NBPSO). In this research, the researcher attempted to solve the problem multi-objectively using Non-Dominated Sorting Native Binary Particle Swarming Optimization (NSNBPSO). In this research, a constraint handling method was proposed called deallocation method which produced schedules that met the requirement of all constraints. In the performance comparison, researcher compared the performance of the proposed NSNBPSO and the previous NBPSO. The metrics used for the comparison were Coverage Rate, Hypervolume and Processing Time.NSNBPSO successfully produced 10 Solutions which provided more options to the stakeholder. However, NSNBPSO produced infeasible solutions and violated constraints in this research. Therefore, deallocation method was proposed and implemented to help NSNBPSO producing feasible solutions. The performance result showed that NSNBPSO with deallocation method had 35% more average hypervolume value than NBPSO, 50% of solutions from NSNBPSO dominated solution from NBPSO in coverage rate evaluation. However, it needed 85% more processing timeandcan still be considered acceptable.

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Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks

Cited by 10 publications

References 20 publications

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

A Challenge for Routing Algorithms in Optical Multistage Interconnection Networks

Multi-Objective Physician Scheduling using Non-Dominated Sorting Native Binary Particle Swarm Optimization with Deallocation Method

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