Sperm motility algorithm: a novel metaheuristic approach for global optimisation

Raouf, Osama Abdel; Hezam, Ibrahim M.

doi:10.1504/ijor.2017.10002079

Cited by 2 publications

(3 citation statements)

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“…In Ref. 15 , the authors have proposed a sperm motility algorithm (SMA) using the principle of chemoattractant secreted by the ovum to guide sperm movement in the female reproductive system. In Ref.…”

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confidence: 99%

“…17 , the authors have proposed a multi-objective sperm fertilization procedure (MOSFP) as a modified form of SSO for multiobjective optimization problems. The general principles of natural conception have been used to develop novel algorithms [15][16][17] . The conception process from the point of view of sperm movement towards the egg in the female reproductive system is modeled in this paper.…”

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A novel Human Conception Optimizer for solving optimization problems

Acharya

Das

2022

Sci Rep

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Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.

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A novel Human Conception Optimizer for solving optimization problems

Acharya

Das

2022

Sci Rep

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“…The mostly used physics-inspired algorithms are gravitational search algorithm (GSA) (Rashedi E, Nezamabadi-Pour H, 2009), atom search optimization (ASO) (Zhao W, Wang L, 2019), electromagnetic field optimization (EFO) (Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, 2016). The human-inspired optimization works based on nature of human behaviour, working of the body parts, communication among humans as used in kidney-inspired algorithm (KA) (Jaddi NS, Alvankarian J, 2017), teaching learning-based optimization (TLBO) (Rao RV, Savsani VJ, 2011), sperm motility algorithm (SMA) (Raouf OA, 2017).…”

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A novel FOPID2FF2 controller optimized by a new Opposition based learning (OBL)-Black Widow Optimization (BWO) algorithm: application to CSTR

v¹,

Prabhakaran²,

Thirumarimurugan³

2022

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In this study a novel controller FOPID2FF2 which is a FOPID controller with two fractional order filter in the two fractional order derivatives was proposed to improve the performance of the Continuous Stirred Tank Reactor (CSTR) system. The proposed controller has nine independent tunable parameters which are optimized by a new metaheuristic algorithm opposition based learning (OBL)-Black widow optimization (BWO). The OBL-BWO chooses its initial population using the opposition based learning (OBL). The OBL helps in improving the exploration capability of the algorithm, avoids the stagnation in local optima, and improves the quality of the initial population for the BWO. The proposed OBL-BWO was tested on standard benchmark functions using the statistical performance and the non-parametric analysis such as Wilcoxon signed rank test, convergence performance was carried out and compared to other state-of-art algorithms. To verify the superiority of the FOPID2FF2 controller optimized using OBL-BWO; FOPID without any filter, FOPID with a integer order filter (FOPIDF), FOPID with one fractional order filter (FOPIDFF) optimized using the same was proposed. The performance of the controller was analysed using the time domain response, frequency domain response and robustnesscriterion. From the statistical analysis the new OBL-BWO was better compared to the state of art algorithms and was used to optimize the FOPID2FF2 controller. From the performance analysis of the mentioned controllers it was identified that the OBL-BWO optimized FOPID2FF2 controller outperforms the other controllers.

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Sperm motility algorithm: a novel metaheuristic approach for global optimisation

Cited by 2 publications

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A novel Human Conception Optimizer for solving optimization problems

A novel Human Conception Optimizer for solving optimization problems

A novel FOPID2FF2 controller optimized by a new Opposition based learning (OBL)-Black Widow Optimization (BWO) algorithm: application to CSTR

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