Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering

Valdez, Fevrier; Castillo, Oscar; Melín, Patricia

doi:10.3390/a14040122

Cited by 41 publications

(16 citation statements)

References 70 publications

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“…The problem of aiming guns was first proposed by Mann in 1958. The purpose of aiming for weapons is to calculate the probability of how we can avoid this attack with minimal damage when certain weapons are attacking [7]. Assigning a target to a weapon is a constrained combinatorial optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Solving Weapon-Target Assignment Problem with Salp Swarm Algorithm

Avcı

Yildirim

2023

Teh. vjesn.

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The weapon target problem is a combinatorial optimization problem. It aims to have the weapons on target properly assigned for the intended purposes. When focused on its target, it does things with its effective attack research in mind. It is an ongoing problem program to minimize survivors. This study, using the weapon target assignment model calculates the expected probabilities on the target with the salp model. The nature of this SHA model is designed to be appropriately predicted for this particular use. The Salp Swarm Algorithm (SSA) is a metaheuristic method of methods approaching the solution set as an approximation. Optimum solution or optimum example is in a working example. This study was done with 12 problem examples (200 training and 200 targets with pleasure to observe, to test the efficiency of SSA). In the problem, the iteration resulted in optimum results at the end of the definite usage time.

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Section: Introductionmentioning

confidence: 99%

Solving Weapon-Target Assignment Problem with Salp Swarm Algorithm

Avcı

Yildirim

2023

Teh. vjesn.

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“…MA is a new algorithm that has not been used before for energy harvesting in WRSN and is a type of optimization algorithm that can accomplish this objective. It is a recently created method that incorporates the key advantages of PSO, GA, and FA, where its superiority in terms of convergence rate and speed was demonstrated in [37,38] and shown to be good. Because it has yet to be implemented in a variety of engineering optimization disciplines, we propose using it to solve energy optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks

Sandrine

Xia

2022

WEVJ

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Wireless energy transfer (WET) is a revolutionary method that has the power to tackle the energy and longevity challenges in wireless sensor networks (WSN). This paper uses a mobile charger (MC) to discover the procedure of WET based on a wireless sensor network (WSN) for a periodic charging technique to maintain the network operational. The goal of this work is to lower overall system energy consumption and total distance traveled while increasing the mobile charger device vacation time ratio. Based on an analysis of total energy consumption, a new metaheuristic called mayfly algorithm (MA) is used to achieve energy savings. Instead of charging all nodes at the same time in each cycle, in our strategy, the mobile charger charges only energy-hungry nodes due to their levels of energy. In this strategy, when the first node reaches the calculated minimum energy, it notifies the base station (BS), which computes all nodes that fall under threshold energy and sends the MC to charge all of them to the maximum energy level in the same cycle. Mathematical results show that the mayfly algorithm can considerably decrease the charging device’s total energy consumption and distance traveled while maintaining performance because it can keep the network operational with less complexity than other schemes.

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“…Other than day-to-day implementations optimization is used on a larger scale too, such as manufacturing of cars in order to minimise wind resistance and maximise speed and handling or designing products in such a way to minimise material cost and maximise the quality and profits, etc. A variety of optimization methods inspired by nature have been developed to solve these problems [26,58]. These algorithms can be classified into four major categories: biology-inspired/bio-inspired, swarm intelligence, socio-inspired and physics/chemistry-based [11].…”

Section: Introductionmentioning

confidence: 99%

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Reddy¹,

Kulkarni²,

Krishnasamy³

et al. 2022

Preprint

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This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behaviour exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (Leader, Advocate, Believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. Besides benchmark problems, the LAB algorithm was applied for solving challenging engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes, and process parameter optimization for turning titanium alloy in a minimum quantity lubrication environment. The results were superior to the other algorithms compared such as Firefly Algorithm, Variations of Co-hort Intelligence, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimisation, and Multi-Cohort Intelligence. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features of the LAB algorithm along with its limitations are also discussed.

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Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering

Cited by 41 publications

References 70 publications

Solving Weapon-Target Assignment Problem with Salp Swarm Algorithm

Solving Weapon-Target Assignment Problem with Salp Swarm Algorithm

Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

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