An Efficient Modification of Grey Wolf Optimization Using Cuckoo Search, Levy Fly and Mantegna Algorithm for Real-Time Image Processing Applications

Sayantan Dutta,Ayan Banerjee

doi:10.15282/ijsecs.7.1.2021.3.0079

Abstract

Optimization methods, frequently used in several image and video processing algorithms for the attainment of optimal solutions, pose severe hurdle in case of real-time processing. For catering to the needs of real-time operations in a cost-effective way, dedicated hardware is inevitable. The huge computational load of any optimization method strikes down its feasibility of being realized in terms of dedicated hardware. The computational complexities of meta-heuristic optimization methods are even more than any other conventional optimization methods. So, in spite of having the capability of providing global solution by dodging local optima, meta-heuristic optimizations are avoided in real-time systems. To overcome the bottleneck, in this article, a modified GWOA (modified grey wolf optimization algorithm)is formulated by blending the advantages of CS (cuckoo search), Levy fly (LV), and MA (Mantegna algorithm). This modified GWOA is articulated to be computationally efficient and precise, so that, it can easily be realized in terms of dedicated VLSI architecture while maintaining the accuracy at a high level. The proposed method helps to diminish the cost and power requirement of high end and costly real-time imaging/ video processing systems while upholding its precision. The proposed method is tested by using MATLAB R2018b. The high-level synthesis (HLS) tool of Xilinx Vivado18.2softwareisusedtosynthesizethisMGWOA, thus establishing the viability of the proposed algorithm to be implemented on FPGA/ ASIC.

Highlights

In the first world countries, there is extensive use of image processing in the major sector, like SI and MI
We have proposed MGWOA in this article
The Modified Grey Wolf Optimization (MGWO) algorithm can able to solve twelve problems out of fifteen problems, but the unselected algorithm is able to solve only three problems out of fifteen problems. If we describe it in terms of percentage, 80%, and 20% are the coverage of MGWO and unselected algorithm

Summary

INTRODUCTION

In the first world countries, there is extensive use of image processing in the major sector, like SI (satellite imaging) and MI (medical imaging). In the field of medical imaging, nature-inspired metaheuristic algorithms are the trending method in recent days. This is used in finding optimum search results. In the year 2014, Mirjalili et al proposed GWO, inspiring from three major steps (searching, encircling, and attacking) [1]. There are many nature-inspired algorithms that have been proposed, but the popularity of the GWO (grey wolf algorithm) remains the same. Our main objective is to find an optimal result For this reason, we have proposed MGWOA (modified grey wolf optimization algorithm) in this article. The GWO algorithm has been concluded (Section VIII)

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