The Aquila Optimizer (AO) is a newly proposed, highly capable metaheuristic algorithm based on the hunting and search behavior of the Aquila bird. However, the AO faces some challenges when dealing with high-dimension...
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The Aquila Optimizer (AO) is a newly proposed, highly capable metaheuristic algorithm based on the hunting and search behavior of the Aquila bird. However, the AO faces some challenges when dealing with high-dimensional optimization problems due to its narrow exploration capabilities and a tendency to converge prematurely to local optima, which can decrease its performance in complex scenarios. This paper presents a modified form of the previously proposed AO, the Locality Opposition-Based Learning Aquila Optimizer (LOBLAO), aimed at resolving such issues and improving the performance of tasks related to global optimization and data clustering in particular. The proposed LOBLAO incorporates two key advancements: the Opposition-Based Learning (OBL) strategy, which enhances solution diversity and balances exploration and exploitation, and the Mutation Search Strategy (MSS), which mitigates the risk of local optima and ensures robust exploration of the search space. Comprehensive experiments on benchmark test functions and data clustering problems demonstrate the efficacy of LOBLAO. The results reveal that LOBLAO outperforms the original AO and several state-of-the-art optimizationalgorithms, showcasing superior performance in tackling high-dimensional datasets. In particular, LOBLAO achieved the best average ranking of 1.625 across multiple clustering problems, underscoring its robustness and versatility. These findings highlight the significant potential of LOBLAO to solve diverse and challenging optimization problems, establishing it as a valuable tool for researchers and practitioners.
This paper studied the multilevel threshold image segmentation-based metaheuristicsoptimization methods and their applications. Image segmentation is a common problem in the image processing domain, and it is an esse...
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This paper studied the multilevel threshold image segmentation-based metaheuristicsoptimization methods and their applications. Image segmentation is a common problem in the image processing domain, and it is an essential process in image analysis, directly impacting image analysis results. Thresholding is one of the most manageable and extensively utilized methods for handling image segmentation problems. In this paper, four main parts are given;(1) We present the main procedures and definitions of the multilevel threshold image segmentation problem. The standard fitness function and the evaluation criteria are also given to facilitate the problem representation for the new researchers in this domain. (2) All the related works that have used optimization methods in solving the multilevel threshold image segmentation problems are presented in more detail, focusing on the image segmentation problem and its solutions. The given related works are outlined according to the used algorithms. (3) Comprehensive results and analysis of several well-known optimizationalgorithms are conducted to solve the multilevel threshold image segmentation problems. These comparative methods include Aquila Optimizer (AO), Whale optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Arithmetic optimization Algorithm (AOA), Particle Swarm Optimizer (PSO), Marine Predators Algorithm (MPA), Krill Herd Algorithm (KHA), Multi-verse Optimizer (MVO), and Gray Wolf Optimizer (GWO). Eight standard benchmark images are used to test the comparative methods. The results are evaluated using three standard measures: fitness function, PeakSignal-to-Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). (4) Discussion, open challenging, and new trends are given to help the scholars in future research get near the common problems and defect in that domain. The collected data in this review has been taken from google scholar using the stander search method. The main keywords that have been use
The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller ...
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The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristicsalgorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies.
Brushless direct current (BLDC) motors are widely used in dynamic applications because of advantages such as high efficiency, wide speed range and low maintenance requirements. The classical Proportional-Integral (PI)...
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Brushless direct current (BLDC) motors are widely used in dynamic applications because of advantages such as high efficiency, wide speed range and low maintenance requirements. The classical Proportional-Integral (PI) control method is generally used for speed control of BLDC motor drivers. Although this method is easy to apply, the determined controller coefficients are generally constant and hence insufficient in dynamic changes. For this reason, methods that respond faster to dynamic changes, such as fuzzy-PI, have been proposed in the literature. Although the rule-based fuzzy controller increases its response ability to dynamic changes, determined rule-based coefficients affects the system performance completely. Therefore, the determination of the rule base values of the fuzzy controller is critical. In this paper, meta-heuristic Cuckoo optimization Algorithm (COA) is proposed to determine the rule base values of the fuzzy controller for BLDC motor. Additionally, the rule-based table values of the fuzzy controller used for BLDC motor is determined using other meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm optimization (PSO), Imperialist Competitive Algorithm (ICA), Invasive Weed optimization (IWO) and the results are compared. Finally, experimental studies for Pittman44 series BLDC motor are also carried out and the results are obtained.
Part-of-Speech (POS) Tagging is the process of automatically determining the proper grammatical tag or syntactic category of a word depending on a its context. POS Tagging is an essential step in most Natural Language...
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Part-of-Speech (POS) Tagging is the process of automatically determining the proper grammatical tag or syntactic category of a word depending on a its context. POS Tagging is an essential step in most Natural Language Processing (NLP) applications such as text summarization, question answering, information extraction and information retrieval. In this study, we propose an efficient tagging approach for the Arabic language using Bee Colony optimization algorithm. The problem is represented as a graph and a novel technique is proposed to assign scores to possible tags of a sentence, then the bees find the best solution path. The proposed approach is evaluated using KALIMAT corpus which consists of 18M words. Experimental results showed that the proposed approach achieved 98.2% of accuracy compared to 98%, 97.4% and 94.6% for Hybrid, Hidden Markov Model and Rule-Based methods respectively. Furthermore, the proposed approach determined all the tags presented in the corpus while the mentioned approaches can identify only three tags. (C) 2018 The Authors. Published by Elsevier B.V.
Part-of-Speech (POS) Tagging is the process of automatically determining the proper grammatical tag or syntactic category of a word depending on a its context. POS Tagging is an essential step in most Natural Language...
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Part-of-Speech (POS) Tagging is the process of automatically determining the proper grammatical tag or syntactic category of a word depending on a its context. POS Tagging is an essential step in most Natural Language Processing (NLP) applications such as text summarization, question answering, information extraction and information retrieval. In this study, we propose an efficient tagging approach for the Arabic language using Bee Colony optimization algorithm. The problem is represented as a graph and a novel technique is proposed to assign scores to possible tags of a sentence, then the bees find the best solution path. The proposed approach is evaluated using KALIMAT corpus which consists of 18M words. Experimental results showed that the proposed approach achieved 98.2% of accuracy compared to 98%, 97.4% and 94.6% for Hybrid, Hidden Markov Model and Rule-Based methods respectively. Furthermore, the proposed approach determined all the tags presented in the corpus while the mentioned approaches can identify only three tags.
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