Image Restoration Based on Hybrid Ant Colony Algorithm

Image restoration is the process to eliminate or reduce the image quality degradation in the digital image formation, transmission and recording and its purpose is to process the observed degraded image to make the restored result approximate the un-degraded original image. This paper, based on the basic ant colony algorithm and integrating with the genetic algorithm, proposes an image restoration processing method based on hybrid ant colony algorithm. This method transforms the optimal population information of genetic algorithm into the original pheromone concentration matrix of ant colony algorithm and uses it to compute the parameters of degradation function so as to get a precise estimate of the original image. By analyzing and comparing the restoration results, the method of this paper can not only overcome the influence of noises, but it can also make the image smoother with no fringe effects in the edges and excellent visual effects, verifying its practicability

An intelligent novel tripartite - (PSO-GA-SA) optimization strategy

A solution approach for many challenging and non-differentiable optimization tasks in industries is the use of non-deterministic meta-heuristic methods. Some of these approaches include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). However, with the implementation usage of these robust and stochastic optimization approaches, there are still some predominant issues such as the problem of the potential solution being trapped in a local minima solution space. Other challenges include the untimely convergence and the slow rate of arriving at optimal solutions. In this research study, a tripartite version (PSO-GA-SA) is proposed to address these deficiencies. This algorithm is designed with the full exploration of all the capabilities of PSO, GA and SA functioning simultaneously with a high level of intelligent system techniques to exploit and exchange relevant population traits in real time without compromising the computational time. The design algorithm further incorporates a variable velocity component that introduces random intelligence depending on the fitness performance from one generation to the other. The robust design is validated with known mathematical test function models. There are substantial performance improvements when the novel PSO-GA-SA approach is subjected to three test functions used as case studies. The results obtained indicate that the new approach performs better than the individual methods from the fitness function deviation point of view and in terms of the total simulation time whilst operating with both a reduced number of generations and populations. Moreover, the new novel approach offers more beneficial trade-off between exploration and exploitation of PSO, GA and SA. This novel design is implemented using an object oriented programming approach and it is expected to be compatible with a variety of practical problems with specified input-output pairs coupled with constraints and limitations on the available resources

A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems

This chapter presents an overview of optimization techniques followed by a brief survey on several swarm-based natural inspired algorithms which were introduced in the last decade. These techniques were inspired by the natural processes of plants, foraging behaviors of insects and social behaviors of animals. These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems

Image Edge Feature Extraction and Refining Based on Genetic-Ant Colony Algorithm

Edge is composed by a collection of its nearby pixels which has a step change or changes in roof, an image is an information system and most of its information comes from the edges. This paper gives a brief overview of the status and the importance of image edge detection and introduces the research status of the image edge detection. After that, it introduces the basic principle and the main steps of the genetic algorithm and ant colony algorithm. On the basis of these, the paper proposed a new hybrid algorithm for the image edge extraction and refining, which combined the genetic algorithm and ant colony algorithm. Through the analysis of the time-speed graph of the genetic algorithm and the ant colony algorithm, we can find the best fusion point between the genetic algorithm and the ant colony algorithm. The experiment indicated the proposed hybrid algorithm can make the full use of the image information, the simulation time is shorter, the image edge is more continuous, and preserved the outline of original image more completely

Color Image Enhancement Based on Ant Colony Optimization Algorithm

In the collection, transmission, decoding process, the images are likely to produce noise. Noise makes the image color distorted and the articulation dropped, and also affects the image quality. Due to different causes, there are different types of noise, and the impulse noise is most common among them which exert great influence on the image quality. This paper, according to the characteristics of the color image, combines the ant colony algorithm and weighted vector median filter method to put forward an algorithm for the impulse noise removal and the color image enhancement. This method finds the optimal filter bank parameter by ant colony optimization (ACO) and processes image points polluted by the noise to achieve the purpose of image enhancement and protect the image details and edge information. Simulation experiment proves the correctness and validity of this method

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex con guration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

من بين الخوارزميات الأدلة العليا (الميتاهيورستك)، تعد الخوارزميات القائمة على البحوث المتعددة (المجتمع) خوارزمية بحث استكشافية متفوقة كخوارزمية البحث المحلية من حيث استكشاف مساحة البحث للعثور على الحلول المثلى العالمية. ومع ذلك، فإن الجانب السلبي الأساسي للخوارزميات القائمة على البحوث المتعددة (المجتمع) هو قدرتها الاستغلالية المنخفضة، مما يمنع توسع منطقة البحث عن الحلول المثلى. خوارزمية اليَرَاعَة المضيئة (Firefly (FA هي خوارزمية تعتمد على المجتمع والتي تم استخدامها على نطاق واسع في مشاكل التجميع. ومع ذلك، فإن FA مقيد بتقاربها السابق لأوانه عندما لا يتم استخدام استراتيجيات بحث محلي لتحسين جودة حلول المجموعات في منطقة المجاورة واستكشاف المناطق العالمية في مساحة البحث. على هذا الأساس، فإن الهدف من هذا العمل هو تحسين FA باستخدام البحث المتغير في الأحياء (VNS) كطريقة بحث محلية (FA-VNS)، وبالتالي توفير فائدة VNS للمفاضلة بين قدرات الاستكشاف والاستغلال. يسمح FA-VNS المقترح لليراعات بتحسين حلول التجميع مع القدرة على تعزيز حلول التجميع والحفاظ على تنوع حلول التجميع أثناء عملية البحث باستخدام مشغلي الاضطراب في VNS. لتقييم أداء الخوارزمية، يتم استخدام ثماني مجموعات بيانات معيارية مع أربع خوارزميات تجميع معروفة. تشير المقارنة وفقًا لمقاييس التقييم الداخلية والخارجية إلى أن FA-VNS المقترحة يمكن أن تنتج حلول تجميع أكثر إحكاما من خوارزميات التجميع المعروفة.Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On these bases, this work aims to improve FA using variable neighborhood search (VNS) as a local search method, providing VNS the benefit of the trade-off between the exploration and exploitation abilities. The proposed FA-VNS allows fireflies to improve the clustering solutions with the ability to enhance the clustering solutions and maintain the diversity of the clustering solutions during the search process using the perturbation operators of VNS. To evaluate the performance of the algorithm, eight benchmark datasets are utilized with four well-known clustering algorithms. The comparison according to the internal and external evaluation metrics indicates that the proposed FA-VNS can produce more compact clustering solutions than the well-known clustering algorithms