Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

Almufti, Saman M.; Marqas, Ridwan Boya; Othman, Pawan Sh.; Sallow, Amira B.

doi:10.24996/10.24996/ijs.2021.62.5.34

Cited by 16 publications

(10 citation statements)

References 11 publications

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“…Numerous academic disciplines, including economics, computer science, engineering, and medicine, have difficulties that naturally requires optimization. Numerous researchers worldwide have focused on the creation of optimization algorithms [4]. The main objective of optimization algorithms, which are often referred to as search methods, is the construction of an ideal solution to an optimization problem such that the provided quantity is optimized subject to a potential set of constraints.…”

Section: Metaheuristicsmentioning

confidence: 99%

Vibrating Particles System Algorithm: Overview, Modifications and Applications

Almufti¹

2022

iij

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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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Section: Metaheuristicsmentioning

confidence: 99%

Vibrating Particles System Algorithm: Overview, Modifications and Applications

Almufti¹

2022

iij

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“…The pheromone trails will guide other ants to the food source. It has been shown by (Almufti, Marqas, Othman, & Sallow, 2021) that the indirect communication between the ants via pheromone trails known as stigmergy enables them to find the shortest paths between their nest and food sources. This is explained in an idealized setting in Figure 1.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

“…TSP is an NP-hard problem in combinatorial optimization (Almufti, Marqas, Othman, & Sallow, 2021). Given a set of cities in which every city must be visited once only and return to the starting city for completing a tour such that the length of the tour is the shortest among all possible tours (Blum, 2005).…”

Section: Traveling Salesman Problem (Tsp)mentioning

confidence: 99%

Ant Based System: Overview, Modifications and Applications from 1992 to 2022

Almufti

Maribojoc²,

Pahuriray³

2022

PGSRT

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The complexity in real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Non-deterministic polynomial hard (NP-Hard) problems because of their ability to adjust to variety of conditions. This paper shows an overview for swarm based algorithm that based on ant behavior. The first algorithm that inspired ant behavior in search for food source was developed in 1992 and was tested in solving TSP problem Ant Colony Optimization (ACO) is a metaheuristic inspired by some ant species' pheromone trail laying and following behavior. Artificial ants in ACO are stochastic solution construction processes that use (artificial) pheromone information that is modified depending on the ants' search experience and possibly accessible heuristic information to generate candidate solutions for the problem instance under consideration. Many notable research achievements have been gained since the proposal of the Ant System, the first ACO algorithm. These contributions concentrated on the creation of high-performance algorithmic variants, the creation of a generic algorithmic framework for ACO algorithms, the successful application of ACO algorithms to a wide range of computationally difficult problems, and the theoretical understanding of ACO algorithm properties. Since the appearance of ACO 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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“…CSO ; Article no.AJRCOS.69197 systems are made up of a population of simple individual's agents interacting locally with each other and with the environment around themselves [1]. During the past years, various successful Swarm Intelligence appears that s of living beings in the nature, such as Ant Colony Optimization (ACO) that inspired by the behavior of Ant in ], Particle Swarm Optimization (PSO) algorithm concept roots from the social behavior of organisms such as fishing ], Bat Colony Optimization (BA) which inspired the bio-inspired sonar or echolocation ], Artificial Bee Colony (ABC) that is inspired from the intelligent, raging behavior of real honey bees in searching for food sources "nectar", and announcing other bees in the nest about the 2,13], Elephant Herding Optimization (EHO) inspired by herding behaviors of elephants in their clan [14,15,16]. According to P. Agarwal and S. Mehta in [17], S.…”

Section: Cat Swarm Optimizationmentioning

confidence: 99%

A Survey on Cat Swarm Optimization Algorithm

Ihsan

Almufti

Ormani³

et al. 2021

AJRCoS

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Swarm based optimization algorithms are a collection of intelligent techniques in the field of Artificial Intelligence (AI) were developed for simulating the intelligent behavior of animals. Over the years ago, problems complexity increased in a means that it is very difficult for basic mathematical approaches to obtain an optimum solution in an optimal time, this leads the researchers to develop various algorithms base on the natural behaviors of living beings for solving problems. This paper present a review for Cat Swarm Optimization (CSO), which is a powerful metaheuristic swarm-based optimization algorithm inspired by behaviors of cats in the Nature for solving optimization problems. Since its first appearances in 2006, CSO has been improved and applied in different fields by many researchers. In this review, we majorly focus on the original CSO algorithm and some improved branches of CSO family algorithms. Some examples of utilizing CSO to solve problems in engineering are also reviewed.

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Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

Cited by 16 publications

References 11 publications

Vibrating Particles System Algorithm: Overview, Modifications and Applications

Vibrating Particles System Algorithm: Overview, Modifications and Applications

Ant Based System: Overview, Modifications and Applications from 1992 to 2022

A Survey on Cat Swarm Optimization Algorithm

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