The  novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications

Almufti, Saman M.

doi:10.46291/icontechvol5iss2pp32-51

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…The collection of solutions in a metaheuristic is too big to sample entirely. Metaheuristics can be used for a range of issues since they may not make many assumptions about the optimization problem being handled(S. M. Almufti, 2022a). In contrast to iterative or optimization techniques, metaheuristics do not ensure that the optimum solution for a given class of problems can be identified.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Overview of Metaheuristic Algorithms

Almufti

Shaban²,

Ali³

et al. 2023

PGSRT

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Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem. Genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search are examples of popular metaheuristic algorithms. These algorithms have been widely utilized to address complicated issues in domains like as engineering, finance, and computer science. In general, the history of metaheuristic algorithms spans several decades and involves the development of various optimization algorithms that are inspired by natural systems. Metaheuristic algorithms have become a valuable tool in solving complex optimization problems in various fields, and they are likely to continue to play an important role in the development of new technologies and applications.

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Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Overview of Metaheuristic Algorithms

Almufti

Shaban²,

Ali³

et al. 2023

PGSRT

View full text Add to dashboard Cite

show abstract

“…Commonly, optimizations refers to finding the best method for enhancing the functionality of the system in question. [20], teaching learning-based optimization (TLBO) [21], Ant Colony Optimization (ACO) [22], Charged System Search (CSS) [23], Fish Swarm Algorithm (FSA) [24], Big Bang Big Crunch (BB-BC) [25], Lion Algorithm (LA) [26], Krill Herd (KH) [27], Elephant…”

Section: Optimizationmentioning

confidence: 99%

Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem

Almufti¹

2022

ACAD J NAWROZ UNIV

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Metaheuristic algorithms are a collection of sophisticated techniques that mimic natural phenomena and the rational behavior of socially intelligent living organisms like insects and animals. These techniques are employed in the fields of computer science and engineering to address various optimization problems. In this paper, the vibrating particles system(VPS) which is a recently developed metaheuristic algorithm. Generally, an under-damped single degree of freedom (SDOF) free vibration oscillates and slowly comes into a resting or equilibrium position, and this is the inspiration idea of VPS. The tension/compression spring design problem, which is a well-known constrained based optimization problem in engineering fields have been used to evaluate VPS algorithm. The experimental result section shows the result of solving the mentioned problem by VPS with various value for variables.

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“…The fundamental issue with the optimization process. Generally, developing new methods for optimizing real world problem had an increasing attention, and as a result, many new metaheuristics algorithms were developed such as Artificial Bee Colony (ABC) [6], Cat Swarm Optimization (CSO) [7], teaching learning-based optimization (TLBO) [8], Colliding Bodies Optimization (CBO) [9] Ant Colony Optimization (ACO) [10], Particle Swarm Optimization (PSO) [11], Charged System Search (CSS) [12], Fish Swarm Algorithm (FSA) [13], Big Bang Big Crunch (BB-BC) [14], Krill Herd (KH) [15], Lion Algorithm (LA) [16], Dolphin Echolocation (DE) [17], Elephant Search Algorithm (ESA) [18], Grey Wolf Optimization (GWO) [19], Cuckoo Search (CS) [20], Vibrating Particles System (VPS) [21], and other optimization algorithms [22]. These algorithm are divided into various categories according to the algorithm inspiration source as shown in Fig( 1) [1].…”

Section: Metaheuristicsmentioning

confidence: 99%

Vibrating Particles System Algorithm: Overview, Modifications and Applications

Almufti¹

2022

iij

Self Cite

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Real-world problems are difficult enough to encourages academics to develop innovative, effective problem-solving methods. Generally, metaheuristics algorithms that are inspired by nature, biology, and physics have a flexibility and capacity to adapt to different situations, metaheuristics based on evolutionary computation algorithms have been widely employed to solve complicated, constrained/unconstrained, single/multiple objective, and Non-deterministic polynomial hard (NP-Hard) optimization problems. This paper describes Vibrating Particles System (VPS) as a Physics Based metaheuristic algorithm inspired by the free vibration of an under-damping objects for solving complex and real-world optimization problems. Since the appearance of VPS many modifications for improving the performance of the algorithm and has been applied to various Applications in several fields. At the end of this paper, the improvements are listed.

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The novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications

Cited by 6 publications

References 42 publications

Overview of Metaheuristic Algorithms

Overview of Metaheuristic Algorithms

Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem

Vibrating Particles System Algorithm: Overview, Modifications and Applications

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