In this paper, a modifiedartificialbeecolony (MABC) algorithm is proposed to estimate the unknown fractional-order nonlinear systems. First, the parameter estimation problem can be mathematically transformed into a...
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In this paper, a modifiedartificialbeecolony (MABC) algorithm is proposed to estimate the unknown fractional-order nonlinear systems. First, the parameter estimation problem can be mathematically transformed into a multi-dimensional continuous function optimization problem, which is solved via the MABC algorithm. The proposed MABC algorithm well combines the original artificialbeecolonyalgorithm (ABC) and the Nelder-Mead simplex method (NMS) in a very simple way, which takes full advantage of the exploration ability of the ABC algorithm and the exploitation ability of the NMS. And in the meanwhile, the proposed algorithm improves the speed of convergence. To evaluate the effectiveness of the proposed MABC algorithm, numerical simulations contain two typical uncertain fractional-order nonlinear systems. The results show that compared with the other population-based algorithm and ABC-variants, the proposed MABC for solving the problem of parameter estimation get the faster convergence speed and higher calculation accuracy.
To develop sustainable groundwater management strategies, generally coupled simulation-optimization (SO) models are used. In this study, a new SO model is developed by coupling moving least squares (MLS)-based meshles...
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To develop sustainable groundwater management strategies, generally coupled simulation-optimization (SO) models are used. In this study, a new SO model is developed by coupling moving least squares (MLS)-based meshless local Petrov-Galerkin (MLPG) method and modifiedartificialbeecolony (MABC) algorithm. The MLPG simulation model utilizes the advantages of meshless methods over the grid-based techniques such as finite difference (FDM) and finite element method (FEM). For optimization, the basic artificialbeecolonyalgorithm is modified to balance the exploration and exploitation capacity of the model more effectively. The performance of the developed MLPG-MABC model is investigated by applying it to hypothetical and field problems with three different management scenarios. The model results are compared with other available SO model solutions for its accuracy. Further, sensitivity analyses of various model parameters are carried out to check the robustness of the SO model. The proposed model gave quite promising results, showing the applicability of the present approach.
Transmission network expansion planning (TNEP) problem is an essential part of power system expansion planning, and it is an extremely complex nonlinear, nonconvex, mixed-integer optimization problem. Solution to such...
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Transmission network expansion planning (TNEP) problem is an essential part of power system expansion planning, and it is an extremely complex nonlinear, nonconvex, mixed-integer optimization problem. Solution to such a computationally intensive problem is a challenge for any optimization algorithm. Consideration of security constraints makes the problem even more formidable. Although various conventional and metaheuristic methods have been used in the past to solve such problem, scope for better optimization techniques always remain. The artificialbeecolony (ABC) algorithm is one of the newest swarm intelligence-based optimization algorithms, which has delivered promising results in solving numerical optimization problems. However, the algorithm is quite less efficient in solving real-life constrained engineering problems. In this paper, a modified ABC (MABC) algorithm is formulated by incorporating the idea of global attraction, universal gravitation, and by introducing modified ways of searching in various bees' phases of the ABC algorithm. The MABC is able to get better results in a very efficient manner, when used for solving various benchmark functions. The efficiency and effectiveness of the MABC algorithm in solving constrained engineering problems is demonstrated by solving TNEP problems for different systems. The proposed method is tested on IEEE 24 bus system, South Brazilian 46 bus system, Colombian 93 bus system for direct current TNEP model, and Garver 6 bus system for alternating current TNEP model. Results confirm that MABC can be an attractive alternative to the existing optimization algorithms for solving very complex nonlinear engineering optimization problems in a real-world situation.
Motivated by the need of reducing power consumption (PC) in two dimensional (2D) finite impulse response (FIR) filters, in this work, the 2D FIR filter design task is formulated as an optimisation problem that seeks t...
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Motivated by the need of reducing power consumption (PC) in two dimensional (2D) finite impulse response (FIR) filters, in this work, the 2D FIR filter design task is formulated as an optimisation problem that seeks to attain the desired frequency response and reduces PC. The optimisation problem has been solved using the modified version of artificialbeecolonyalgorithm. The applicability of the proposed approach has been evaluated by designing circular shaped 2D FIR filters for a set of specifications in frequency domain. The designed filters have been compared with other reported state of the art techniques. The evaluation is carried out in terms of pass band and stop band ripple minimisation, convergence profile and PC during filter execution in hardware. The proposed technique is found to outperform all other techniques in achieving minimum ripple for a given filter order. To prove the effectiveness of the proposed approach for PC reduction, the designed filters have been implemented in hardware using field-programmable gate array (xc7vx485t-2ffg1761). The PC computed using Xilinx X-power analyser shows that 23.53% power can be saved using the proposed approach as compared with conventional design approaches.
In order to reduce investment and maintenance cost, the optimal sizing of distributed energy in AC/DC hybrid stand-alone micro-grid is studied in this paperThe optimization model is built and economics, power supply r...
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In order to reduce investment and maintenance cost, the optimal sizing of distributed energy in AC/DC hybrid stand-alone micro-grid is studied in this paperThe optimization model is built and economics, power supply reliability and environmental protection are taken into accountBy means of the modified artificial bee colony algorithm, the optimal sizing method is obtainedFinally, the feasibility of method is verified by calculating example analysisBy comparing the optimal sizing results of three different micro-grid configurations, hybrid AC/DC micro-grid is proved to be superior to traditional micro-gridFurthermore, the modified ABC is more suitable for solving optimization problems because of the higher convergence precision.
In order to overcome the disadvantages of the K-Means Clustering algorithm, such as the poor global search ability, being sensitive to initial cluster centric, as well as the vulnerable to trap in local optima and the...
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ISBN:
(纸本)9781479918430
In order to overcome the disadvantages of the K-Means Clustering algorithm, such as the poor global search ability, being sensitive to initial cluster centric, as well as the vulnerable to trap in local optima and the slow convergence velocity in later period of the original artificialbeecolony (ABC) algorithm, a modified ABC algorithm was proposed. modified artificial bee colony algorithm combined with K-means Clustering algorithm, named it as MABC-K-means algorithm, to establish Hybrid algorithm for solving framework. Through extensive testing, the MABC-K-means algorithm can improve cluster performance effectively. Finally, according to optimization solution strategy, instantiate Customer Relationship Management issue in the process of instantiating framework.
As cloud computing and the big data industry continue to expand, the demand for faster data processing speed is also on the rise. This has led to the emergence of a distributed processing resource scheduling system. H...
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ISBN:
(纸本)9798350387780;9798350387797
As cloud computing and the big data industry continue to expand, the demand for faster data processing speed is also on the rise. This has led to the emergence of a distributed processing resource scheduling system. However, resource scheduling faces major challenges such as resource allocation issues, excessively long processing times, high costs, and uneven workload distribution. In response to these challenges, this article proposes an enhanced approach using the modified artificial bee colony algorithm (MABC). This algorithm incorporates inertia weights and global optimization components, expanding the algorithm's search range and enhancing its stability. By effectively addressing the difficulties in resource scheduling, it achieves multi-objective optimization results.
Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly diffic...
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Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid modifiedartificialbeecolony and Firefly algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.
This paper presents an application of modified artificial bee colony algorithm (MABC) to determine the optimized solution of economic and emission load dispatch (EELD) problem. The EELD problem is formulated as a biob...
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ISBN:
(纸本)9781479906871;9781479906888
This paper presents an application of modified artificial bee colony algorithm (MABC) to determine the optimized solution of economic and emission load dispatch (EELD) problem. The EELD problem is formulated as a biobjective problem by taking minimization of fuel cost and emission levels as objectives. In order to convert a biobjective problem into a single objective function weighing factor is used. Effectiveness of the MABC algorithm is verified by applying it on five standard test systems and the outcomes are compared with the latest reported literatures. It is proved from the results that MABC algorithm is more powerful than other algorithms.
artificialbeecolony (ABC) algorithm, explored in recent literature, is an efficient optimization technique which simulates the foraging behavior of honeybees. ABC algorithm is good at exploration but poor at exploit...
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ISBN:
(纸本)9781467327138
artificialbeecolony (ABC) algorithm, explored in recent literature, is an efficient optimization technique which simulates the foraging behavior of honeybees. ABC algorithm is good at exploration but poor at exploitation. This paper presents a new modified ABC algorithm for numerical optimization problems to improve the exploitation capability of the ABC algorithm. A different probability function and a new searching mechanism are proposed. The modified ABC algorithm is tested on seven numerical optimization problems. The results demonstrate that the modified ABC algorithm outperforms the ABC algorithm on solution quality and faster convergence.
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