Applicability and Efficiency of Socio-Cultural Inspired Algorithms in Optimizing Mechanical Systems – A Critical Review

Patel, Sanket; Kakandikar, Ganesh M.; Kulkarni, Omkar

doi:10.18280/rces.070203

Cited by 3 publications

(2 citation statements)

References 51 publications

(53 reference statements)

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“…Nevertheless, this bug takes nearly a yearly to be born; passes away after a maximal of one period day of life. The major goal of their childbirth is coupling [26]. A number of them are not even bothered to eat [3].…”

Section: Introductionmentioning

confidence: 99%

Mayfly optimization algorithm: a review

Bogar,

Shirodkar,

Kulkarni

et al. 2024

J. mechatron., artif. intell., eng.

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This paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on .One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future.

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Section: Introductionmentioning

confidence: 99%

Mayfly optimization algorithm: a review

Bogar,

Shirodkar,

Kulkarni

et al. 2024

J. mechatron., artif. intell., eng.

Self Cite

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“…Nowadays, there are many evolutionary and Bat inspired optimization algorithms available, such as Grey Wolf Optimizer (GWO) [2], Particle Swarm Optimization (PSO) [3], Ant Colony Optimization (ACO) [4], Grasshopper Optimization Algorithm (GOA) [5], Firefly Algorithm (FA) [6], Salp Swarm Algorithm (SSA) [7], Cuckoo Search Algorithm (CS) [8], Genetic Algorithm (GA) [9], etc. Socially inspired algorithms such as Cohort Intelligence (CI) [10] and ideology algorithm [11] are also algorithms that help find the best parameters for the problem. Bat-inspired algorithms [12] have their own set of benefits and functions, which are explored in the following sections.…”

Section: Introductionmentioning

confidence: 99%

Process parameters optimization by bat inspired algorithm of CNC turning on EN8 steel for prediction of surface roughness

Burande¹,

Kulkarni²,

Jawade³

et al. 2021

J. mechatron., artif. intell., eng.

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Due to its high precision, productivity, and surface quality, computer numerical control turning (CNC) is a desirable processing tool in the traditional processing area. CNC machining procedures have a huge number of process parameters, making it challenging to find the best combination of parameters for increased accuracy. In this research work, the Taguchi method and ANOVA were used to study the effects of CNC machining parameters in EN8 steel turning: Surface roughness (Ra) value of component affected due to cutting speed, depth of cut and feed rate. Three-level three-parameter experimental design, using Minitab 17 software using L9 orthogonal array, using coated carbide insert cutting tools, using signal-to-noise ratio (S/N) to study the performance characteristics of EN8 steel turning. In this study, statistical approaches such as the signal-to-noise ratio (S/N ratio) and analysis of variance (ANOVA) were used to explore the effects of cutting speed, depth of cut, and feed rate on surface roughness. Natureinspired algorithms play a vital role in solving real life. In this study, the bat algorithm can be used to predict the optimal surface value (Ra) and process parameters. Verify the results by conducting confirmation experiments. The current research shows that the feed rate is the most important factor affecting the surface roughness (Ra) of EN8 steel turning.

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