Improving Metaheuristic Performance by Evolving a Variable Fitness Function

Dahal, Keshav; Remde, Stephen; Cowling, Peter I.; Colledge, Nic

doi:10.1007/978-3-540-78604-7_15

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Almada-Lobo et al (2008) report the use of a VNS approach to production planning and scheduling in the glass container industry. Dahal et al (2008) apply a constructive search and VNS to tackle a complex real world workforce scheduling problem. Abraham et al (2008) propose a VNS/PSO hybrid for the scheduling problem in distributed data-intensive computing environments.…”

Section: Car Sequencingmentioning

confidence: 99%

Variable neighbourhood search: methods and applications

2009

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Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley. It was first proposed in 1997 and has since then rapidly developed both in its methods and its applications. In the present paper, these two aspects are thoroughly reviewed and an extensive bibliography is provided. Moreover, one section is devoted to newcomers. It consists of steps for developing a heuristic for any particular problem. Those steps are common to the implementation of other metaheuristics.

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Section: Car Sequencingmentioning

confidence: 99%

Variable neighbourhood search: methods and applications

2009

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“…Thus, more effective functions lead to significantly better results [16] [17]. The fitness function must be defined according to problem features, i.e., the objective is represented by a particular feature capable of evaluating candidate solutions in terms of their goodness and suitability for solving the problem [18].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Investigating Fitness Functions for Search-based Requirements Prioritization

Corezolla

Costa

Souza

et al. 2021

Anais Do XII Computer on the Beach - COTB '21

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It can be challenging for people to select the most relevant requirementamong several software system development options.Requirements prioritization defines the ordering for executing requirementsbased on their priority or importance concerning stakeholders’viewpoints, which is a problematic task. Based on thisproblem, this study aims to present a requirements prioritizationapproach using a genetic algorithm to find optimal solutions, andit can assist in the requirements prioritization activity during thesoftware development process. In this paper, we investigated aset of criteria to create four functions GUT-D, ThS-D, ST, and LT,to assess candidate solutions, i.e., the recommended prioritizedrequirements. We examine the empirical results concerning thepractical approach’s effectiveness and cost computational two experimentsin the evaluation. We found that the 𝐺𝑈𝑇 − 𝐷 fitnessfunction achieved the best fitness value in different settings with90.51% and 98.63%. Besides that, our study demonstrates that the approachis promising to assist requirements prioritization since eachfitness function can be used individually according to companies’necessities.

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“…The SCA evolves through the solution space typically using a modification of the objective function named fitness function [39]. This helps deal with possible infeasibilties of the decision variables [40]. However, due to the continuous part for the problem is formulated as a SOCP model; most of the constraints are directly fulfilled during the solution procedure via interior point methods.…”

Section: Fitness Function Evaluationmentioning

confidence: 99%

A Hybrid Approach Based on SOCP and the Discrete Version of the SCA for Optimal Placement and Sizing DGs in AC Distribution Networks

et al. 2020

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This paper deals with the problem of the optimal placement and sizing of distributed generators (DGs) in alternating current (AC) distribution networks by proposing a hybrid master–slave optimization procedure. In the master stage, the discrete version of the sine–cosine algorithm (SCA) determines the optimal location of the DGs, i.e., the nodes where these must be located, by using an integer codification. In the slave stage, the problem of the optimal sizing of the DGs is solved through the implementation of the second-order cone programming (SOCP) equivalent model to obtain solutions for the resulting optimal power flow problem. As the main advantage, the proposed approach allows converting the original mixed-integer nonlinear programming formulation into a mixed-integer SOCP equivalent. That is, each combination of nodes provided by the master level SCA algorithm to locate distributed generators brings an optimal solution in terms of its sizing; since SOCP is a convex optimization model that ensures the global optimum finding. Numerical validations of the proposed hybrid SCA-SOCP to optimal placement and sizing of DGs in AC distribution networks show its capacity to find global optimal solutions. Some classical distribution networks (33 and 69 nodes) were tested, and some comparisons were made using reported results from literature. In addition, simulation cases with unity and variable power factor are made, including the possibility of locating photovoltaic sources considering daily load and generation curves. All the simulations were carried out in the MATLAB software using the CVX optimization tool.

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Improving Metaheuristic Performance by Evolving a Variable Fitness Function

Cited by 7 publications

References 13 publications

Variable neighbourhood search: methods and applications

Variable neighbourhood search: methods and applications

Investigating Fitness Functions for Search-based Requirements Prioritization

A Hybrid Approach Based on SOCP and the Discrete Version of the SCA for Optimal Placement and Sizing DGs in AC Distribution Networks

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