Overview of Metaheuristic Algorithms

Almufti, Saman M.; Shaban, Awaz Ahmad; Ali, Rasan Ismael; Fuente, Jayson A. Dela

doi:10.58429/pgjsrt.v2n2a144

Cited by 27 publications

(8 citation statements)

References 38 publications

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“…According to M. Almufti et al, 2023), there were more than 200 Metaheuristic algorithms have been developed to address a wide range of practical problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Almufti

2023

ACAD J NAWROZ UNIV

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A Metaheuristic Optimization is a group of algorithms that are widely studied and employed in the scientific literature. Typically, metaheuristics algorithms utilize stochastic operators that make each iteration unique, and they frequently contain controlling parameters that have an impact on the convergence process since their impacts are mostly neglected in most optimization literature, making it difficult to draw conclusions. This paper introduced the Big Bang-Big Crunch (BB-BC) metaheuristic algorithm to evaluate the performance of a metaheuristic algorithm in relation to its control parameter. It also demonstrates the effects of varying the values of BB-BC in solving. The "Welded Beam Design problem" is a well-known engineering optimization problem that is classified as a Single-Objective Constrained Optimization issue. Multiple starting parameter values for the BB-BC are evaluated as part of the experimental findings. This is done in an attempt to find the algorithm's optimal starting settings. The lowest, maximum, and mean values of the penalized objective functions are then computed. Finally, the BB-BC results are compared with various metaheuristics algorithms.

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“…According to M. Almufti et al, 2023), there were more than 200 Metaheuristic algorithms have been developed to address a wide range of practical problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Almufti

2023

ACAD J NAWROZ UNIV

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“…These systems often exhibit emergent properties and can solve complex problems through simple interactions. While there are various ways to categorize swarm intelligence, the main categories shown in fig (2) [18]: -Ant Colony Behavior: Ant colonies demonstrate efficient foraging, nest-building, and trailfollowing behaviors. Researchers study these behaviors to gain insights into robust and scalable systems for tasks like resource allocation, routing, or task allocation in distributed systems.…”

Section: Figure 1 Si Component a Categories Of Simentioning

confidence: 99%

“…As a result, the quality of the initial solutions is evaluated. ABC pseudo code is shown below in (algorithm 3): The Ant Colony Optimization (ACO) is a heuristic algorithm inspired from the behavior of real ant in finding the shortest way to a source of food [1,2,6]. Naturally, ants in a swarm are indirectly communicates by an odorous chemical substance that ants may deposit and smell called pheromone trails.…”

Section: Algorithm 1: Pseudocode Of the Basic Psomentioning

confidence: 99%

“…Such systems are made up of a population of simple individual's agents interacting locally with each other and with the environment around themselves [1]. This paper focuses on the comparative analysis of most successful methods of optimization techniques inspired by Swarm Intelligence (SI): Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), Bat Colony Optimization (BA), and Artificial Bee Colony (ABC) [2]. Ant Colony Optimization (ACO) is a population based optimization algorithm developed by Marco Dorigo as an inspiration of the behavior of ants in finding the optimal way (best path) between their nest and a food source [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

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Review of Swarm Intelligence for Solving Symmetric Traveling Salesman Problem

Shaban

Fuente

Salih

et al. 2023

QAJ

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Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. In this article we are applying most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.

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“…Metaheuristics have garnered significant attention in the realm of computational intelligence for their ability to offer solutions to complex optimization problems that traditional algorithms struggle with 19 . These algorithms, often inspired by natural or sociological phenomena, aim to strike a balance between exploration (searching new areas in the solution space) and exploitation (refining current solutions).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing daylight in west-facing facades for LEED V4.1 compliance using metaheuristic approach

Son,

Huyen

2023

Sci Rep

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This study introduces an optimized design approach for west-facing room façades to improve daylighting while adhering to LEED v4.1 sustainability criteria. Employing parametric modeling, metaheuristic optimization, and validated daylight simulations, the research highlights the African Vulture Optimization Algorithm's success in achieving 100% LEED compliance and superior performance over random models in daylight sufficiency and glare reduction. Light-colored materials and transparent glazing emerged as beneficial for LEED points. Despite computational limitations and the need for empirical validation, this method offers architects versatile and sustainable design solutions. Comparative analysis reveals the algorithm's strong performance, although opportunities exist for refinement. Future research directions include contrasting this algorithm with other optimization methods, focusing on empirical backing, assessing environmental and human-centric impacts, adapting to varied building types and conditions, and examining diverse geographical and material factors. This work advances daylight-integrated façade design, suggesting a more comprehensive framework for building performance optimization.

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Overview of Metaheuristic Algorithms

Cited by 27 publications

References 38 publications

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution

Review of Swarm Intelligence for Solving Symmetric Traveling Salesman Problem

Optimizing daylight in west-facing facades for LEED V4.1 compliance using metaheuristic approach

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