A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Mohammadi, Soleiman Kadkhoda; Nazarpour, Daryoush; Beiraghi, Mojtaba

doi:10.1007/s00521-023-08229-1

Cited by 7 publications

(2 citation statements)

References 117 publications

(138 reference statements)

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“…Azizi et al [24] proposed a novel metaheuristic algorithm-Energy Valley Optimizer (EVO), and it can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems. Kadkhoda et al [25] proposed the coronavirus metamorphosis optimization algorithm (CMOA), and the CMOA is applied to three engineering problems including optimal design of a welded beam, a three-bar truss and a pressure vessel, showing its high potential in solving such practical problems and effectiveness in finding global optima.…”

Section: Introductionmentioning

confidence: 99%

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

Fu,

Wu,

Wang

et al. 2024

CMES

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In this paper, given the shortcomings of jellyfish search algorithm with low search ability in the early stage and easy to fall into local optimal solution, this paper introduces adaptive weight function and elite strategy, improving the global search scope in the early stage and the ability to refine the local development in the later stage. In the numerical study, the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted, and the corresponding penalty method is used for constraint treatment. The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights. Because when the steel main truss of the large-span covered bridge is lifted, the site is limited and the large lifting equipment cannot enter the site, and the original structure does not meet the problem of stress concentration and large deformation of the bolt group, so the spreader is used to lift, and the improved jellyfish search algorithm is introduced into the design optimization of the spreader. The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader, and through Midas Civil simulation, the spreader used can meet the requirements of weight and safety.

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Section: Introductionmentioning

confidence: 99%

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

Fu,

Wu,

Wang

et al. 2024

CMES

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“…Currently, there are eight distinct meta-heuristic optimization algorithms, each of which incorporates a unique aspect of the infamous pandemic. These are not just theoretical explorations but practical innovations named as follows: the Coronavirus Optimization Algorithm (CVOA) [19], the Coronavirus Herd Immunity Optimizer (CHIO) [20], the Corona Virus Optimization (CVO) [21], the Anti-Coronavirus Optimization (ACVO) algorithm [22], the Coronavirus Optimization Algorithm (COVIDOA) [23], the COVID-19 Based Optimization Algorithm (C-19BOA) [24], the Coronavirus Metamorphosis Optimization Algorithm (CMOA) [25], and the Coronavirus Mask Protection Algorithm (CMPA) [26]. These algorithms demonstrate their notably improved performance in solving various optimization challenges.…”

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confidence: 99%

Enhancing Individual UAV Path Planning With Parallel Multi-Swarm Treatment Coronavirus Herd Immunity Optimizer (PMST-CHIO) Algorithm

Fouad,

Abboudi,

Huyck

et al. 2024

IEEE Access

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This paper introduces the PMST-CHIO, a novel variant of the Coronavirus Herd Immunity Optimizer (CHIO) algorithm, exclusively tailored for individual unmanned aerial vehicle (UAV) path planning in complex 3D environments. While acknowledging and building upon the foundational principles derived from UAV swarm path planning research, the PMST-CHIO distinctively focuses on optimizing the trajectory of single UAVs. It innovatively integrates a parallel multi-swarm treatment mechanism, enhancing the standard CHIO's exploration and exploitation capabilities significantly. This mechanism diverges from the swarm-based approaches by deploying multiple instances of the CHIO optimizer, each functioning autonomously within its sub-swarm, thereby facilitating independent path planning for individual UAVs. These multiple CHIO instances or CHIO candidates, operate in concert to determine the optimal and collision-free routes, taking into account the unique characteristics of individual UAVs and the intricacies of the service area. The algorithm incorporates two key mechanisms: 1) global exploitation, employing the best solution identified by the highest performing CHIO candidate across the swarms; and 2) strategic shift from parallel multi-swarm exploration to focused exploration by the top-performing CHIO candidate after a specific iteration threshold is reached. This adaptation significantly improves the algorithm's global search efficiency, convergence behavior, and navigational accuracy under challenging environments. Extensive simulations and comparative studies validate that the PMST-CHIO can effectively overcome the limitations of the standard CHIO algorithm, yielding safer, shorter, and more compliant flight paths for individual UAVs in intricate 3D landscapes.INDEX TERMS Coronavirus Herd Immunity Optimizer (CHIO), flight path optimization and safety, unmanned aerial vehicles (UAVs) path planning. I. INTRODUCTIONUnmanned Aerial Vehicles (UAVs) are autonomous flight systems that can operate under remote control or viaThe associate editor coordinating the review of this manuscript and approving it for publication was Yang Tang . integrated onboard computers. These vehicles provide a costeffective, adaptable, and safer alternative to manned aircraft, which are particularly useful in hazardous or inaccessible areas. UAVs have gained extensive application in both civilian and military sectors due to these advantages. In civilian applications, they are instrumental in tasks like agricultural

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Coronavirus Metamorphosis Optimization Algorithm and Collocation Method for Optimal Control Problem in COVID‐19 Vaccination Model

Jajarmi,

Ebrahimzadeh,

Khanduzi

2024

Optim Control Appl Methods

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This article addresses an optimal control problem (OCP) related to the COVID‐19 vaccination model. We transform the OCP into a nonlinear programming (NLP) problem by employing a collocation method that utilizes shifted Jacobian polynomials (SJPs) and their derivative operational matrices. To solve the NLP, we implement the Coronavirus Metamorphosis Optimization Algorithm (CMOA), which determines the control variables for , 2, and 3, corresponding to isolation, vaccination efficacy, and treatment enhancement. The CMOA is particularly suitable for the OCP concerning COVID‐19 due to its ability to navigate complex challenges and provide accurate solutions. Therefore, the primary aim of this paper is to identify a high‐quality optimal solution for the OCP, thereby contributing to the development of effective control strategies for managing the COVID‐19 pandemic.

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A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Cited by 7 publications

References 117 publications

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

Enhancing Individual UAV Path Planning With Parallel Multi-Swarm Treatment Coronavirus Herd Immunity Optimizer (PMST-CHIO) Algorithm

Coronavirus Metamorphosis Optimization Algorithm and Collocation Method for Optimal Control Problem in COVID‐19 Vaccination Model

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