Modified Shuffled Frog Leaping Algorithm by adaptive Step Size: Applications to Constraint Engineering Design Problems
Bhagyashri Naruka1, Ashwani Kumar Yadav2, Vaishali3, Shweta Sharma4, Janesh Singh Rathore35

1Bhagyashri Naruka, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India.
2Ashwani Kumar Yadav, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India.
3Vaishali, School of Computing and Information Technology, Manipal University Jaipur, India,
4Shweta Sharma, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India.
5Janesh Singh Rathore, Body Engineering, Hero Motocorp, Jaipur, India.

Manuscript received on 23 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 1093-1098 | Volume-8 Issue-11, September 2019. | Retrieval Number: J11790881019/2019©BEIESP | DOI: 10.35940/ijitee.J1179.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Shuffled frog leaping algorithm (SFLA) is an ongoing expansion to the group of evolutionary algorithm that imitates the societal and natural conduct of species. Upsides of particle swarm optimization (PSO) and shuffled complex evolution (SCE) is consolidates in SFLA i.e. local searching and information shuffling respectively. In this paper SFLA is improved to solve equality and inequality based constraint engineering design problems using penalty function. In proposed approach linear decreasing function that is adaptive in nature will be utilized to improve worst frog position for better exploration and convergence speed. The simulation results designate the superiority of present study over SFLA in term of global optimum solution and fast convergence rate.
Keywords: Shuffled Frog leaping Algorithm (SFLA), Linear Decreasing function, Constraint Engineering Design Problems, Constraint Handling, Penalty Function.
Scope of the Article: