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RISS 인기검색어
- 검색키워드
- 저자 : Sankalap Arora (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Chaotic grey wolf optimization algorithm for constrained optimization problems
Mehak Kohli,Sankalap Arora 한국CDE학회 2018 Journal of computational design and engineering Vol.5 No.4
The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The perfor-mance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimiza-tion problems.
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Chaotic whale optimization algorithm
Kaur, Gaganpreet,Arora, Sankalap Society for Computational Design and Engineering 2018 Journal of computational design and engineering Vol.5 No.3
The Whale Optimization Algorithm (WOA) is a recently developed meta-heuristic optimization algorithm which is based on the hunting mechanism of humpback whales. Similarly to other meta-heuristic algorithms, the main problem faced by WOA is slow convergence speed. So to enhance the global convergence speed and to get better performance, this paper introduces chaos theory into WOA optimization process. Various chaotic maps are considered in the proposed chaotic WOA (CWOA) methods for tuning the main parameter of WOA which helps in controlling exploration and exploitation. The proposed CWOA methods are benchmarked on twenty well-known test functions. The results prove that the chaotic maps (especially Tent map) are able to improve the performance of WOA.
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Chaotic whale optimization algorithm
Gaganpreet Kaur,Sankalap Arora 한국CDE학회 2018 Journal of computational design and engineering Vol.5 No.3
The Whale Optimization Algorithm (WOA) is a recently developed meta-heuristic optimization algorithm which is based on the hunting mechanism of humpback whales. Similarly to other meta-heuristic algo-rithms, the main problem faced by WOA is slow convergence speed. So to enhance the global conver-gence speed and to get better performance, this paper introduces chaos theory into WOA optimization process. Various chaotic maps are considered in the proposed chaotic WOA (CWOA) methods for tuning the main parameter of WOA which helps in controlling exploration and exploitation. The proposed CWOA methods are benchmarked on twenty well-known test functions. The results prove that the chaotic maps (especially Tent map) are able to improve the performance of WOA.
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Performance Evaluation of Image Enhancement Techniques
Shruti Puniani,Sankalap Arora 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.8
Color Image enhancement is a process in which the perceptual information of an image is improved to obtain more information and details contained in the image. It improves the subjective quality of an image by working with original data. This paper focuses on evaluating the performance of various image enhancement techniques. These techniques are either based on histogram modification or are based on fuzzy logic. The techniques are compared using two quantitative measures namely; Contrast Improvement index (CII) and Tenengrad measure. The results have shown that Lab and edge preservation based fuzzy image enhancement (LEFM) yields the best results.
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