Optimization Algorithms Surpassing Metaphor

Samadi-Koucheksaraee, Arvin; Shirvani-Hosseini, Seyedehelham; Ahmadianfar, Iman; Gharabaghi, Bahram

doi:10.1007/978-981-19-2519-1_1

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…Over time, other metaheuristic methods based on different approaches have emerged (Samadi-Koucheksaraee et al 2022). Simulated Annealing (SA), which simulates the annealing process in metallurgy to find near-optimal solutions, was firstly introduced by Sousa et al (1970).…”

Section: Introductionmentioning

confidence: 99%

Review of Metaheuristic Methodologies for Leakage Reduction and Energy Saving in Water Distribution Networks

Ferrarese,

Medoukali,

Mirauda

et al. 2024

Water Resour Manage

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Metaheuristic methods have emerged as powerful tools for solving complex optimization problems in various domains, including the sustainability of water distribution systems. They provide efficient and effective solutions by mimicking natural processes and searching for the optimal option within a large solution space. Despite the existence of these methods in the water distribution field for several years, a direct comparison between the various proposed solutions often proves challenging, due to the different parameter definitions used by the authors. The present review presents the solutions proposed by a total of 36 research papers taken from the Web of Science, Scopus and Google Scholar databases focusing on the application of metaheuristic methods for leakage reduction and energy saving in water distribution networks. The review is intended to facilitate comparative analysis among the solutions proposed by authors concerning key aspects of the optimization process. These aspects include the definition of the algorithm, the specification of the objective function, and the strategies employed for reducing the search space. The characteristics of the networks used as case studies by the reviewed papers are also presented to allow the reader to evaluate the applicability of the solutions to specific networks.

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Section: Introductionmentioning

confidence: 99%