Metaheuristics: a comprehensive overview and classification along with bibliometric analysis

Ezugwu, Absalom E.; Shukla, Amit K.; Nath, Rahul; Akinyelu, Andronicus Ayobami; Agushaka, Jeffrey O.; Chiroma, Haruna; Muhuri, Pranab K.

doi:10.1007/s10462-020-09952-0

Cited by 127 publications

(47 citation statements)

References 309 publications

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“…Meta-heuristics are general-purpose optimization algorithms [ 22 ], where the term meta refers to the higher-level general methodologies, which are used to guide the underlying heuristic strategy [ 23 ]. Meta-heuristics are highly diverse in nature, see [ 24 , 25 , 26 ] and references therein for overviews. In the context of the present paper, a highly promising type of meta-heuristics is offered by the concept of greedy randomized adaptive search procedures (GRASP), which is based on an iterative approach consisting of two phases: (1) a construction phase, and (2) a local search phase.…”

Section: Conclusion Discussionmentioning

confidence: 99%

A Data-Driven Digital Application to Enhance the Capacity Planning of the COVID-19 Vaccination Process

et al. 2021

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In this paper, a decision support system (DSS) is presented that focuses on the capacity planning of the COVID-19 vaccination process in the Netherlands. With the Dutch national vaccination priority list as the starting point, the DSS aims to minimize the per-class waiting-time with respect to (1) the locations of the medical hubs (i.e., the vaccination locations) and (2) the distribution of the available vaccines and healthcare professionals (over time). As the user is given the freedom to experiment with different starting positions and strategies, the DSS is ideally suited for providing support in the dynamic environment of the COVID-19 vaccination process. In addition to the DSS, a mathematical model to support the assignment of inhabitants to medical hubs is presented. This model has been satisfactorily implemented in practice in close collaboration with the Dutch Municipal and Regional Health Service (GGD GHOR Nederland).

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Section: Conclusion Discussionmentioning

confidence: 99%

A Data-Driven Digital Application to Enhance the Capacity Planning of the COVID-19 Vaccination Process

et al. 2021

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“…Heuristics based algorithms are utilized heavily to find a solution for the JSSP based problems. In varied research topics, several heuristics and meta-heuristics taxonomies have been introduced for optimization an algorithms family [22][23][24][25][26][27], however, most of those taxonomies are influenced by relatively old anatomy. As an actively updated field, the recent years hold new discoveries in optimization techniques paired with a synchronous implementation in application based approaches.…”

Section: Heuristicsmentioning

confidence: 99%

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

Awad

Abd-Elaziz

2021

Processes

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In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.

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“…Nature has inspired many metaheuristic algorithms; they solve optimization problems by mimicking natural phenomena. These phenomena cover a range of natural processes from such areas as biology, physics, chemistry and swarms (population-based) [ 1 , 2 ]. The bio-inspired metaheuristic algorithms are frequently inspired by the laws of natural evolution.…”

Section: Introductionmentioning

confidence: 99%

Advanced arithmetic optimization algorithm for solving mechanical engineering design problems

Agushaka

Ezugwu

2021

PLoS ONE

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The distributive power of the arithmetic operators: multiplication, division, addition, and subtraction, gives the arithmetic optimization algorithm (AOA) its unique ability to find the global optimum for optimization problems used to test its performance. Several other mathematical operators exist with the same or better distributive properties, which can be exploited to enhance the performance of the newly proposed AOA. In this paper, we propose an improved version of the AOA called nAOA algorithm, which uses the high-density values that the natural logarithm and exponential operators can generate, to enhance the exploratory ability of the AOA. The addition and subtraction operators carry out the exploitation. The candidate solutions are initialized using the beta distribution, and the random variables and adaptations used in the algorithm have beta distribution. We test the performance of the proposed nAOA with 30 benchmark functions (20 classical and 10 composite test functions) and three engineering design benchmarks. The performance of nAOA is compared with the original AOA and nine other state-of-the-art algorithms. The nAOA shows efficient performance for the benchmark functions and was second only to GWO for the welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD).

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Metaheuristics: a comprehensive overview and classification along with bibliometric analysis

Cited by 127 publications

References 309 publications

A Data-Driven Digital Application to Enhance the Capacity Planning of the COVID-19 Vaccination Process

A Data-Driven Digital Application to Enhance the Capacity Planning of the COVID-19 Vaccination Process

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

Advanced arithmetic optimization algorithm for solving mechanical engineering design problems

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