scholarly journals Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3187
Author(s):  
Nien-Che Yang ◽  
Danish Mehmood ◽  
Kai-You Lai
Keyword(s):  
Power Systems ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Manhattan Distance ◽  
Third Order ◽  
Multi Objective ◽  
Power Filters ◽  
Bee Colony ◽  
Passive Power

Passive power filters (PPFs) are most effective in mitigating harmonic pollution from power systems; however, the design of PPFs involves several objectives, which makes them a complex multiple-objective optimization problem. This study proposes a method to achieve an optimal design of PPFs. We have developed a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm with a minimum Manhattan distance. Four different types of PPFs, namely, single-tuned, second-order damped, third-order damped, and C-type damped order filters, and their characteristics were considered in this study. A series of case studies have been presented to prove the efficiency and better performance of the proposed method over previous well-known algorithms.

scholarly journals Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 133
Author(s):  
Nien-Che Yang ◽  
Danish Mehmood
Keyword(s):  
Power Systems ◽  
Optimization Problems ◽  
Filter Design ◽  
Manhattan Distance ◽  
Multi Objective Optimization ◽  
Optimal Method ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Passive Power

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

Stellar Mass Black Hole for Engineering Optimization

2017 ◽  
pp. 62-90
Author(s):  
Premalatha Kandhasamy ◽  
Balamurugan R ◽  
Kannimuthu S
Keyword(s):  
Black Hole ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Stellar Mass ◽  
Bee Colony ◽  
Nature Inspired Algorithms ◽  
The Universe

In recent years, nature-inspired algorithms have been popular due to the fact that many real-world optimization problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to develop an optimization method whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. A black hole is an object that has enough masses in a small enough volume that its gravitational force is strong enough to prevent light or anything else from escaping. Stellar mass Black hole Optimization (SBO) is a novel optimization algorithm inspired from the property of the gravity's relentless pull of black holes which are presented in the Universe. In this paper SBO algorithm is tested on benchmark optimization test functions and compared with the Cuckoo Search, Particle Swarm Optimization and Artificial Bee Colony systems. The experiment results show that the SBO outperforms the existing methods.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures

2012 ◽  
pp. 59-84
Author(s):  
S. N. Omkar ◽  
G. Narayana Naik ◽  
Kiran Patil ◽  
Mrunmaya Mudigere
Keyword(s):  
Design Optimization ◽  
Failure Criterion ◽  
Composite Structures ◽  
Composite Plate ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Failure Criteria ◽  
Multi Objective ◽  
Bee Colony ◽  
Swarm Algorithms

In this paper, a generic methodology based on swarm algorithms using Artificial Bee Colony (ABC) algorithm is proposed for combined cost and weight optimization of laminated composite structures. Two approaches, namely Vector Evaluated Design Optimization (VEDO) and Objective Switching Design Optimization (OSDO), have been used for solving constrained multi-objective optimization problems. The ply orientations, number of layers, and thickness of each lamina are chosen as the primary optimization variables. Classical lamination theory is used to obtain the global and local stresses for a plate subjected to transverse loading configurations, such as line load and hydrostatic load. Strength of the composite plate is validated using different failure criteria—Failure Mechanism based failure criterion, Maximum stress failure criterion, Tsai-Hill Failure criterion and the Tsai-Wu failure criterion. The design optimization is carried for both variable stacking sequences as well as standard stacking schemes and a comparative study of the different design configurations evolved is presented. Performance of Artificial Bee Colony (ABC) is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for both VEDO and OSDO approaches. The results show ABC yielding a better optimal design than PSO and GA.

scholarly journals Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

2021 ◽  
Vol 12 (1) ◽  
pp. 27
Author(s):  
Hassan Shokouhandeh ◽  
Sohaib Latif ◽  
Sadaf Irshad ◽  
Mehrdad Ahmadi Kamarposhti ◽  
Ilhami Colak ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Reactive Power Compensation ◽  
Voltage Profile ◽  
Bee Colony ◽  
Control Devices

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures

2011 ◽  
Vol 2 (3) ◽  
pp. 1-26 ◽  
Author(s):  
S. N. Omkar ◽  
G. Narayana Naik ◽  
Kiran Patil ◽  
Mrunmaya Mudigere
Keyword(s):  
Design Optimization ◽  
Failure Criterion ◽  
Composite Structures ◽  
Composite Plate ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Failure Criteria ◽  
Line Load ◽  
Multi Objective ◽  
Bee Colony

In this paper, a generic methodology based on swarm algorithms using Artificial Bee Colony (ABC) algorithm is proposed for combined cost and weight optimization of laminated composite structures. Two approaches, namely Vector Evaluated Design Optimization (VEDO) and Objective Switching Design Optimization (OSDO), have been used for solving constrained multi-objective optimization problems. The ply orientations, number of layers, and thickness of each lamina are chosen as the primary optimization variables. Classical lamination theory is used to obtain the global and local stresses for a plate subjected to transverse loading configurations, such as line load and hydrostatic load. Strength of the composite plate is validated using different failure criteria—Failure Mechanism based failure criterion, Maximum stress failure criterion, Tsai-Hill Failure criterion and the Tsai-Wu failure criterion. The design optimization is carried for both variable stacking sequences as well as standard stacking schemes and a comparative study of the different design configurations evolved is presented. Performance of Artificial Bee Colony (ABC) is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for both VEDO and OSDO approaches. The results show ABC yielding a better optimal design than PSO and GA.

scholarly journals An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 289
Author(s):  
Songyi Xiao ◽  
Wenjun Wang ◽  
Hui Wang ◽  
Dekun Tan ◽  
Yun Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Computational Results ◽  
Bee Colony ◽  
Elite Strategy ◽  
Large Jump ◽  
Complex Optimization

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

scholarly journals Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-37 ◽  
Author(s):  
Wenping Zou ◽  
Yunlong Zhu ◽  
Hanning Chen ◽  
Beiwei Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Difficult Problem ◽  
Standard Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Flight Direction ◽  
Bee Colony

Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

Implementation of an Artificial Bee Colony to Solve an Order Picking Problem

2019 ◽  
pp. 144-160
Author(s):  
Luis Enrique Cisneros Saucedo ◽  
Julia Patricia Sanchez-Solis ◽  
Francisco López-Ramos ◽  
Jorge Rodas-Osollo
Keyword(s):  
Nonprofit Organizations ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Order Picking ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Limited Budget ◽  
Study Case ◽  
Parameter Values

The artificial bee colony (ABC) algorithm is an optimization method based on swarm intelligence which has demonstrated to be capable of obtaining satisfactory results on a diversity of optimization problems. However, the implementation of this optimization method hasn't been much explored on order picking problems, even though order picking represents up to 55% of the total operational cost of a typical warehouse. The order picking problem has even more importance on nonprofit organizations like food banks since they operate with a limited budget. In this chapter, the authors implemented an ABC algorithm to solve the order picking problem within a food bank. The goal was to determine which parameter values contribute the most during the optimization process. Experiments were conducted using nine sets of parameters for the ABC; results show that the approach is suitable for the study case.

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