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Different Optimization Algorithms for Optimal Coordination of Directional Overcurrent Relays
Published in Baseem Khan, Sanjeevikumar Padmanaban, Hassan Haes Alhelou, Om Prakash Mahela, S. Rajkumar, Artificial Intelligence-Based Energy Management Systems for Smart Microgrids, 2022
Ahmed Korashy, Salah Kamel, Abdel-Raheem Youssef, Francisco Jurado
Recently, meta-heuristic and hybrid optimization methods have been developed and most widely used in the coordination of relay to get the globally optimal solution and able to escape from local minima solutions [31] such as:Particle Swarm Optimization (PSO) that simulated hunting mechanism for birds in nature [32–35].Genetic Algorithm (GA) that inspired from concepts of Darwinian evolution [34–36].Seeker algorithm that simulated memory of human and experience of social learning [19].Ant Colony Optimization (ACO) simulate the searching mechanism of an ant for the shortest path between a source of food and their home colony [37], [38].Harmony Search (HS) inspired from searching for a perfect state of harmony during the process of getting music composition [15], [33].
Swarm Optimization and Machine Learning to Improve the Detection of Brain Tumor
Published in Shikha Agrawal, Manish Gupta, Jitendra Agrawal, Dac-Nhuong Le, Kamlesh Kumar Gupta, Swarm Intelligence and Machine Learning, 2022
Ant Colony Optimization (ACO) is a swarm intelligence algorithm that has been developed using the foraging behavior of ants. The ants deposit pheromone on the ground while moving from one place to the other and rest of the ants follow the same path. This mechanism of the ants has been used by the ACO in solving optimization problems. It uses a number of artificial ants that determine solutions to the given optimized problems. The well known travelling salesman problem uses ACO to find the shortest path to the given set of cities [15]. ACO is based on iterations. For any ACO algorithm, a number of ants are considered for every iteration. The artificial ants find a solution by traversing the fully constructed graph. The vertices of the construction graph represent the solution components on which the pheromone is deposited. At each iteration, a number of solutions are constructed by the ants. Local search is then used to improve the obtained solutions. Applying local search is problem specific and optional but used in most of the ACO algorithms. After this the pheromone values are updated, by which the pheromones related to the promising or good solutions can be increased and those related to the bad solutions can be decreased. The good results of ACO algorithms have made them appealing for numerous applications [16]. This section will discuss, ACO algorithms used for the segmentation of MRI images.
Evolutionary Programming and Heuristic Optimization
Published in James A. Momoh, Adaptive Stochastic Optimization Techniques with Applications, 2015
Ant colony optimization (ACO) is a class that is applied to combinatorial optimization problems. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions [6,7]. ACO uses computational concurrent and asynchronous agents called a colony of ants, which move through states of the problem corresponding to partial solutions of the problem to solve. The measurement generally involves a stochastic local decision policy based on two parameters, called trails and attractiveness. The pheromone information will direct the search of the future. When an ant completes a solution during the construction phase, it evaluates the solution and modifies the trail value on the components used in its solution.
Metaheuristic Algorithms in Smart Farming: An Analytical Survey
Published in IETE Technical Review, 2023
The ant colony optimization algorithm (ACO) is a probabilistic procedure for tackling computational issues. Marco Dorigo proposed this optimization algorithm in 1992. Soil total nitrogen estimation by [38] was done by ant colony optimization and machine learning [39]. Bioethanol production from sorghum grains is proposed in [31] by using an artificial neural network and ant colony optimization with ethanol concentration and yield are 82.11 g/L and 93 percent, respectively. A hybrid evolutionary ant colony optimization technique proposed by [40] improves the efficiency of the adsorption rate derived from waste biomass (sugarcane bagasse). Ant Colony Optimization (ACO) is a metaheuristic algorithm used for solving optimization problems. Its advantages include robustness, flexibility, adaptivity, and simplicity. However, ACO may converge slowly, requires parameter tuning, is sensitive to initialization, and may have scalability issues.
A hybrid metaheuristic algorithm to achieve sustainable production: involving employee characteristics in the job-shop matching problem
Published in Journal of Industrial and Production Engineering, 2023
Bingtao Quan, Sujian Li, Kuo-Jui Wu
Ant colony optimization (ACO) is an optimization algorithm that simulates ant colony food-seeking behavior. Compared with other heuristic algorithms, ACO has strong robustness in solving performance and can search for better solutions [25]. Based on the optimization characteristics of ACO and the idea of ant coloring, Nguyen and Jung [26], solved the multisource and multidestination routing problem. The simulation results show that the path method based on (ACO) is superior to other methods. Similarly, ACO can be applied to scheduling problems. In the resource allocation of a dynamic cloud environment, an improved ACO is utilized to optimize the task scheduling of load balancing; the simulation test results show that this method has the advantages of lower execution time, operation cost and delay [27]. In the scheduling of network communication services, to achieve the dual optimizing goals of the virtual equipment layout and the operating cost, a virtual equipment integration optimization based on ACO was proposed to solve the problem in real time [28]. Although prior studies have applied ACO to resolve different problems, most of them are resource matching problems, which reflects the superior performance of the algorithm in this respect. Therefore, this study uses ACO to solve the problem of employee-job matching.
Optimization of reservoir operation at Klang Gate Dam utilizing a whale optimization algorithm and a Lévy flight and distribution enhancement technique
Published in Engineering Applications of Computational Fluid Mechanics, 2021
V. Lai, Y. F. Huang, C. H. Koo, Ali Najah Ahmed, Ahmed El-Shafie
Swarm intelligence is a well-known representative model for aggregate behaviors that arise as a result of local interactions between individual components and their environment. Several well-known algorithms in this group include the following: ant colony optimization (ACO) simulates the social activity of ants when they follow the shortest path between their nest and a food supply. Particle swarm optimization (PSO) is focused on the combined navigation and hunting activity of birds. Al-Aqeeli and Mahmood Agha (2020) applied PSO to the formulation of two different multi- and single-reservoir models in the two areas at Mosul Badush of Northern Iraq. In this analysis, the aim is to determine the optimum activity policies, leveraging annual maximum hydroelectric power. The reservoir process optimization of the Dez reservoir system in Iran was introduced using ACO, which resulted in a sensitive global optimal solution sensitive (Jalali et al., 2006).