Applications of computational intelligence‐based systems for societal enhancement

Caraffini, Fabio; Chiclana, Francisco; Moodley, Raymond; Góngora, Mario

doi:10.1002/int.22862

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The latter optimisation paradigms feature several heuristics that can be used over black-box problems, or any other challenging optimisation problem for which using an exact method would not be viable or time-efficient. For this reason, such heuristics approaches do not only represent an interesting fundamental research topic, but have also become very popular among practitioners benefiting from their wide-scope applicability in several domains of sciences, engineering and industry [3][4][5][6]; however, in the vast majority of cases, the literature reporting on such application studies is written from an academic point of view, i.e., highlighting novelty and technical aspects, often overlooking the impact created by the proposed technology in industry in more practical terms. In this study, we instead focus on showing that the introduction of well-known heuristic optimisation in technological devices can improve some characteristics of the product, making it not only more efficient, but also more usable, thus improving upon customer satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System

Ryan¹,

Kühn

Colreavy‐Donnelly

et al. 2022

Applied Sciences

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We demonstrate that particle swarm optimisation (PSO) can be used to solve a variety of problems arising during operation of a digital inspection microscope. This is a use case for the feasibility of heuristics in a real-world product. We show solutions to four measurement problems, all based on PSO. This allows for a compact software implementation solving different problems. We have found that PSO can solve a variety of problems with small software footprints and good results in a real-world embedded system. Notably, in the microscope application, this eliminates the need to return the device to the factory for calibration.

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Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System

Ryan¹,

Kühn

Colreavy‐Donnelly

et al. 2022

Applied Sciences

Self Cite

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Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of Lanzhou City

Li,

Tang,

et al. 2024

Appl Intell

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Evolving routing policies for electric vehicles by means of genetic programming

Gil-Gala,

Đurasević,

Jakobović

2024

Appl Intell

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In recent years, the growing interest in environmental sustainability has led to Electric Vehicle Routing Problems (EVRPs) attracting more and more attention. EVRPs involve the use of electric vehicles, which have additional constraints, such as range and recharging time, compared to conventional Vehicle Routing Problems (VRPs). The complexity and dynamic nature of solving VRPs often lead to the introduction of Routing Policies (RPs), simple heuristics that incrementally build routes. However, manually designing efficient RPs proves to be a challenging and time-consuming task. Therefore, there is a pressing need to explore the application of hyper-heuristics, in particular Genetic Programming (GP), to automatically generate new RPs. Since this method has not yet been investigated in the literature in the context of EVRPs, this study explores the applicability of GP to automatically generate new RPs for EVRP. To this end, three RP variants (serial, semiparallel, and parallel) are introduced in this study, along with a set of domain-specific terminal nodes to optimise three criteria: the number of vehicles, energy consumption, and total tardiness. The experimental analysis shows that the serial variant performs best in terms of energy consumption and number of vehicles, while the parallel variant is most effective in minimising the total tardiness. A comprehensive analysis of the proposed method is conducted to determine its convergence properties and the impact of the proposed terminal nodes on performance and to describe several generated RPs. The results show that the automatically generated RPs perform commendably compared to traditional methods such as metaheuristics and exact methods, which usually require significantly more runtime. More specifically, depending on the scenario in which they are used, the generated RPs achieve results that are about 20%-37% worse compared to the best known results for the number of vehicles in almost negligible time, in just some milliseconds.

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Applications of computational intelligence‐based systems for societal enhancement

Cited by 4 publications

References 11 publications

Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System

Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System

Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of Lanzhou City

Evolving routing policies for electric vehicles by means of genetic programming

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