Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems

Xian, Sidong; Feng, Xiao

doi:10.1016/j.eswa.2023.120482

Cited by 31 publications

(8 citation statements)

References 39 publications

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“…This source provides a comprehensive account of how the algorithm's performance was rigorously assessed, its effectiveness in meeting specified criteria, and its empirical substantiation through experimentation on a diverse set of mathematical functions. The findings presented in reference [26] serve to affirm the algorithm's viability and its ability to fulfill the intended objectives across a range of scenarios and mathematical contexts.…”

Section: Death and Rebirthmentioning

confidence: 69%

“…The validation, satisfaction, and empirical validation of the proposed algorithm across various mathematical functions are comprehensively detailed in reference [26]. This source provides a comprehensive account of how the algorithm's performance was rigorously assessed, its effectiveness in meeting specified criteria, and its empirical substantiation through experimentation on a diverse set of mathematical functions.…”

Section: Death and Rebirthmentioning

confidence: 99%

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Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Jokar,

Niknam,

Dehghani

et al. 2023

Energies

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Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

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Section: Death and Rebirthmentioning

confidence: 69%

Section: Death and Rebirthmentioning

confidence: 99%

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Jokar,

Niknam,

Dehghani

et al. 2023

Energies

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“…Li et al [30] introduced an improved balanced optimizer based on multi-strategy optimization, which dynamically balances the exploration and development phases, demonstrating enhanced solving efficiency in engineering optimization problems. Xian et al [31] presented a meerkat optimization algorithm that exhibits effectiveness and superiority in solving real engineering optimization problems with constraints. Abualigah et al [12] introduced an arithmetic optimization algorithm utilizing mathematical modeling with the main arithmetic operators.…”

Section: Related Workmentioning

confidence: 99%

Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

Zhou,

Shi,

Wang

et al. 2024

Mathematics

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This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

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“…For performance evaluation, four engineering design problems including, (1) pressure vessel design, (2) rolling element bearing design, (3) tension/compression spring design, and (4) cantilever beam design, are used. The MHDE algorithm is compared with respect to some of the well-known algorithms including, artificial rabbit optimization (ARO) 55 , taguchi search algorithm (TSA) 56 , multi-strategy chameleon algorithm (MCSA) 56 , hybrid particle swarm optimization (HPSO) 57 , equilibrium optimizer (EO) 21 , evolution strategies (ES) 58 , grasshopper optimization algorithm (GOA) 59 , ( ) evolutionary search (ES) 60 , harris hawk optimizer (HHO) 56 , cuckoo search (CS) 55 , GCAII 55 , ant colony optimization (ACO) 55 , co-evolutionary DE (CDE) 60 , bacterial foraging optimization algorithm (BFOA) 61 , symbiotic optimization search (SOS) 62 , passing vehicle search (PVS) 63 , meerkat optimization algorithm (MOA) 64 , red panda optimizer (RPO) 65 , mine blast algorithm (MBA) 66 , moth flame optimizer (MFO) 56 , thermal exchange optimization (TEO) 67 , GCAI 55 , co-evolutionary differential evolution (CDE) 60 , seagull optimization algorithm (SOA) 68 , co-evolutionary particle swarm optimization approach (CPSO) 57 , and dynamic opposition strategy taylor-based optimal neighbourhood strategy and crossover operator (DTCSMO) 69 .…”

Section: Real-world Applications I: Engineering Design Problemsmentioning

confidence: 99%

“…The outcomes pertaining to this design problem are presented in Table 6 , where the results are evaluated using different algorithms for comparative analysis. These algorithms encompass MCSA 56 , ARO 55 , CPSO 75 , HPSO 57 , ( )ES 60 , ACO 55 , CDE 60 , HHO 56 , MOA 64 , RPO 65 , MFO 56 , TSA 55 , MVO 56 , and others. The convergence patterns are shown in Fig.…”

Section: Real-world Applications I: Engineering Design Problemsmentioning

confidence: 99%

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Salgotra,

Gandomi

2024

Sci Rep

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Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.

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Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems

Cited by 31 publications

References 39 publications

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

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