Multi-objective Shark Smell Optimization for Solving the Reactive Power Dispatch Problem

Bagheri, Mehdi; Sultanbek, Adilet; Abedinia, Oveis; Naderi, Mohammad Salay; Naderi, Mehdi Salay; Ghadimi, Noradin

doi:10.1109/eeeic.2018.8494502

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…Few creatures on the planet now elicit such awe and horror. 30 What is the cause for this crea- • The fish (prey) is injured, and its blood is injected into the water (search). As a result, fish movement is slow and insignificant in comparison to shark motion.…”

Section: Shark Smell Optimization (Sso)mentioning

confidence: 99%

A fuzzy‐based method for cloud service migration using a shark smell optimization algorithm

Liu

Cheng

et al. 2022

Concurrency and Computation

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Computation should develop and become more powerful and flexible as a result of the expansion of apps and the incorporation of novel consumers into the realm of computing systems. The potential of live virtual machine (VM) migration among various clouds is one of the growing study fields in cloud computing. It can be more important when the cloud service quality that a user is presently using deteriorates or when a new cloud service is launched that is superior in performance, quality, and pricing to the existing services. Because this problem is NP-Hard in nature, this article proposes a method to migrate the load from an over-loaded VMs to the active machine that is least loaded using a fuzzy-based shark smell optimization algorithm. This algorithm is based on a shark's ability to discover prey as a strong hunter in nature, based on the shark's smell sense and motion to the odor source. The suggested optimization technique mathematically models diverse shark behaviors in the search environment, which is seawater.The CloudSim is utilized to illustrate the efficiency of the approach compared to others. The outcomes reveal that energy consumption and execution time are better than Genetic Algorithm and Particle Swarm Optimization algorithms.

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Section: Shark Smell Optimization (Sso)mentioning

confidence: 99%

A fuzzy‐based method for cloud service migration using a shark smell optimization algorithm

Liu

Cheng

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…[39][40][41] In recent years, various types of meta-heuristics have been suggested that are inspired by different natural phenomena. For example, artificial bee colony optimization algorithm, 42 shark smell optimization, 43 quantum invasive weed optimization, 41 and world cup optimization algorithm. 39 In 2016, Razmjooy et al proposed a new optimization algorithm based on the FIFA world cup competitions; this algorithm is called world cup optimization (WCO) algorithm.…”

Section: Basic World Cup Optimization Algorithmmentioning

confidence: 99%

Using chaos world cup optimization algorithm for medical images contrast enhancement

Wang

Liu

Jimenez

2019

Concurrency and Computation

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In this paper, a chaos-based optimization algorithm is employed for medical images contrast enhancement. Here, a weighted combined framework is suggested as the cost function for the contrast enhancement. The method utilizes the advantages of Gamma correction, histogram equalization, and edge information for decreasing the original image features losses. For more enhancements, a piecewise version of Gamma correction is also utilized to decrease the unnatural artifacts in the output image. A combined cost function is employed based on the three aforementioned features and the proposed chaos world cup optimization algorithm is used for maximizing the fitness function. The simulation results have been compared with five state-of-the-art methods for presenting the method efficiency. To do this, contrast, homogeneity, weighted average peak SNR, a measure of enhancement, and contrast noise ratio are employed. The results also applied to two standard medical imaging datasets and compared with the other methods. Final results denoted that the presented multiobjective optimization algorithm improves the quality of the image contrast and can illustrate more details and information toward the other compared methods.

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A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

Sreedharan

Pete

2020

Int J Communication

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Summary One of the famous approaches to decision making is named as multicriteria decision making (MCDM). In order to solve the MCDM issues, a better way is provided by the fuzzy logic. Expendability, cost, maintenance, availability of software, and performance characteristics are such problems considered by the decision. The precise estimation of the pertinent data is one of the vital phases in DM systems. This paper presents a fuzzy MCDM‐based cluster head (CH) selection and hybrid routing protocol to solve the most common issues. In this research article, the generalized intuitionistic fuzzy soft set (GIFSS) approach is utilized to select the optimal CH, and hybrid shark smell optimization (SSO), and a genetic algorithm (GA) is introduced for the effective routing. Initially, the wireless sensor network (WSN) system and energy models are designed, and then the nodes are grouped into several clusters. Next, based on the GIFSS, the CH nodes are selected, and finally, an effective routing is placed based on the hybrid optimizations. The implementation is performed on the NS2 platform, and the performances are evaluated by packet delivery ratio (PDR), delay, packet loss ratio (PLR), network lifetime, bit error rate (BER), energy consumption, throughput, and jitter. The existing approaches named energy centers examining using particle swarm optimization (EC‐PSO), variable dimension‐based PSO (VD‐PSO), energy‐efficient PSO‐based CH selection (PSO‐ECHS), low‐energy adaptive clustering hierarchy‐sugeno fuzzy (LEACH‐SF), SSO, and GA are compared with the proposed strategy. According to the implemented outcomes, it displays the proposed strategy and gives improved outcomes than the others.

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Multi-objective Shark Smell Optimization for Solving the Reactive Power Dispatch Problem

Cited by 10 publications

References 14 publications

A fuzzy‐based method for cloud service migration using a shark smell optimization algorithm

A fuzzy‐based method for cloud service migration using a shark smell optimization algorithm

Using chaos world cup optimization algorithm for medical images contrast enhancement

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

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