Review of Metaheuristics Inspired from the Animal Kingdom

Drăgoi, Elena Niculina; Dafinescu, Vlad

doi:10.3390/math9182335

Cited by 35 publications

(22 citation statements)

References 428 publications

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“…These studies aim to identify all relevant evidence on the topic/area or research question. Up to now, many such studies were published in Computer Science journals or conferences [48][49][50][51]. They are found to be useful for educational purposes as well, providing a good starting point for a PhD work [52].…”

Section: Systematic Reviewmentioning

confidence: 99%

Attraction Basins in Metaheuristics: A Systematic Mapping Study

2021

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Context: In this study, we report on a Systematic Mapping Study (SMS) for attraction basins in the domain of metaheuristics. Objective: To identify research trends, potential issues, and proposed solutions on attraction basins in the field of metaheuristics. Research goals were inspired by the previous paper, published in 2021, where attraction basins were used to measure exploration and exploitation. Method: We conducted the SMS in the following steps: Defining research questions, conducting the search in the ISI Web of Science and Scopus databases, full-text screening, iterative forward and backward snowballing (with ongoing full-text screening), classifying, and data extraction. Results: Attraction basins within discrete domains are understood far better than those within continuous domains. Attraction basins on dynamic problems have hardly been investigated. Multi-objective problems are investigated poorly in both domains, although slightly more often within a continuous domain. There is a lack of parallel and scalable algorithms to compute attraction basins and a general framework that would unite all different definitions/implementations used for attraction basins. Conclusions: Findings regarding attraction basins in the field of metaheuristics reveal that the concept alone is poorly exploited, as well as identify open issues where researchers may improve their research.

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Section: Systematic Reviewmentioning

confidence: 99%

Attraction Basins in Metaheuristics: A Systematic Mapping Study

2021

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“…Researchers have recently proposed new optimization algorithms to solve various problems. (Mirjalili and Lewis 2016;Dragoi andDafinescu 2021, Karami et al 2021;. So, using optimization algorithms to calculate simulation parameters can lead to increase modeling accuracy.…”

Section: Introductionmentioning

confidence: 99%

Hydrological drought analysis in response to climate change based on a novel hybrid machine learning algorithm

Morshed-Bozorgdel

Hosseini

Farzin

2022

Preprint

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For the first time, a combination of metaheuristic algorithms and machine learning is used for hydrological drought analysis under climate change conditions and applications. The new framework is used by a novel hybrid machine learning model named the least-squares support vector machine-African vulture optimization algorithm (LSSVM-AVOA). The performance of the proposed hybrid algorithm was compared with three algorithms, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machines (LSSVM). Using the technique for order of preference by similarity to the ideal solution (TOPSIS) method showed that LSSVM-AVOA with a score of 0.98 could be employed to improve the LSSVM modeling results. Three global climate models (GCM), including ACCESS-ESM1-5 (A1), CanESM5 (C5), and MRI-ESM2-0 (M2) during the historical period 1985–2014 and four scenarios, including SSP126, SSP245, SSP245-cov-fossil (SCF), SSP585 in future from 2021 to 2050 was considered for predicting discharge at Karun basin (Sepid Dasht Sezar, Bakhtiari Tang Pang, Sezar Tang Pang, Taleh Zang and Gotvand stations). Using new hybrid algorithm, the prediction results showed that discharge in most scenarios and stations decreased between − 0.81% and − 7.83% (except Sezar Tang Pang and Gotvand station). The standardized runoff index (SRI) results of hydrological drought analysis showed by the SSP585 scenario it seems that for the next first 10 years, a mild drought period can occur in this basin. Also, in the future period for SSP126 scenario by the first five years in the next 30 years, none drought is predicted.

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“…Figure 6. Distribution of inspiration sources of meta-heuristics in the literature (Dragoi and Dafinescu, 2021)…”

Section: List Of Figuresmentioning

confidence: 99%

“…Fig. 6 shows a summary of the percentage contribution of different sources of inspiration for the meta-heuristics in the literature (Dragoi and Dafinescu, 2021).…”

Section: Potential Of State-of-the-art Meta-heuristicsmentioning

confidence: 99%

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Optimal sizing of microgrids: A comparison of herd-behaviour-oriented meta-heuristics

Khalid¹

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The strategic long-term planning and design optimisation of renewable and sustainable energy systems, and particularly microgrids (MGs), is essential for the most effective use of limited resources, especially as the deployment of localised, distributed energy generation systems increases. The main objective of such integrated resource planning exercises is to minimise total discounted system costs, whilst adhering to a set of interlinked technical constraints, including reliability. This results in a non-deterministic polynomial time-hard (NP-hard) problem, for which no polynomial time solution exists. This has therefore brought to light the importance of utilising meta-heuristic optimisation algorithms. Meta-heuristics are higher-level general strategies inspired by natural phenomena, which can be adapted to yield a near-globally-optimum solution to NP-hard problems by iteratively improving the position of candidate solutions under a pre-defined measure of quality or time. Given the approximate nature of meta-heuristics, they have been found to have different efficiencies in different applications due to the fundamental differences in the form of the underlying objective functions – and the nonlinearities and non-convexities involved. Accordingly, testing the efficiency of new meta-heuristics in different areas is an active research area. In this context, a review of the MG sizing literature has identified that the performance of a number of state-of-the-art, herd-behaviour-oriented meta-heuristics has not yet been addressed, namely the wild horse optimiser (WHO), the artificial hummingbird algorithm (AHA), the artificial gorilla troops optimiser (AGTO), the marine predator algorithm (MPA), the equilibrium optimiser (EO), and the moth-flame optimisation algorithm (MFOA). In response, this study carried out a systematic performance comparison of the above-mentioned algorithms by benchmarking them against the well-established meta-heuristic in the literature, namely the particle swarm optimisation (PSO). To this end, two stand-alone battery-supported MGs were modelled, which provide an efficient solution for the electrification of personal passenger and utility fleets, in addition to serving residential and commercial loads. The first MG integrates solar photovoltaic (PV) and wind resources, while the second MG is solely driven by solar PV panels – both backed by battery storage. Moreover, to effectively coordinate the charge scheduling of integrated electric vehicles (EVs) – for improved cost solutions – specific rule-based dispatch strategies were developed. The conceptual MGs were then populated for three communities residing on Aotea–Great Barrier Island, in Aotearoa–New Zealand, who currently suffer from the unreliability of privately purchased, smaller-scale renewable energy systems. The comparative summary-statistics-based results obtained from the application of the proposed method, parametrised for the two MG configurations of interest, to the three community cases, reveal the important role of newly advanced meta-heuristics in optimising a statistically robust, minimum cost solution to the associated MG asset allocation problem. Comprehensive capital budgeting, cash flow, and energy flow analyses, as well as various univariate sensitivity analyses, have, furthermore, verified the validity and effectiveness of the proposed optimised systems in providing an integrated, reliable, affordable, clean, secure platform for serving residential, commercial, and EV-charging loads in remote and island communities.

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Review of Metaheuristics Inspired from the Animal Kingdom

Cited by 35 publications

References 428 publications

Attraction Basins in Metaheuristics: A Systematic Mapping Study

Attraction Basins in Metaheuristics: A Systematic Mapping Study

Hydrological drought analysis in response to climate change based on a novel hybrid machine learning algorithm

Optimal sizing of microgrids: A comparison of herd-behaviour-oriented meta-heuristics

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