scholarly journals Artificial Bee Colony Algorithm based on a New Local Search Approach for Parameter Estimation of Photovoltaic Systems

2021 ◽  
Author(s):  
Mehmet fatih Fatih TEFEK
Keyword(s):  
Parameter Estimation ◽  
Local Search ◽  
Artificial Bee Colony ◽  
Pso Algorithm ◽  
Photovoltaic Systems ◽  
Search Procedure ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Comparison Results ◽  
Local Search Procedure

Abstract In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and PV modules of PV systems for parameter estimations. The success of the ABC-Ls method was compared with the algorithms commonly used in the literature such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm and standard ABC algorithm, and the comparison results were presented in tables and graphics in detail. It was found that the ABC-Ls method was more successful and achieved better results compared to GA, PSO and standard ABC. It was determined that the ABC-LS method performed similar or better PV parameter estimation compared to other studies in the literature.

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 561-600
Author(s):  
Selcuk Aslan
Keyword(s):  
Big Data ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Abc Algorithm ◽  
Bee Colony ◽  
De Algorithm ◽  
Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

scholarly journals Self-Adaptive Artificial Bee Colony for Function Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhu Tang ◽  
Wen Long ◽  
Huawei Wu ◽  
Kang Zhang ◽  
Yuri A. W. Shardt
Keyword(s):  
Local Search ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Initial Population ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Global And Local ◽  
Self Adaptive

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

scholarly journals Hybridization of Artificial Bee Colony Algorithm and its Variants with Hyperbolic Spiral based Local Search

2019 ◽  
Vol 9 (2) ◽  
pp. 3547-3551
Keyword(s):  
Local Search ◽  
Honey Bees ◽  
Efficient Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategies ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Swarming Behavior

Artificial bee colony (ABC) algorithm is grounded on intelligent swarming behavior of honey bees. It is one of the efficient algorithm for optimization. The ABC algorithm is good in exploration and sometimes fails to exploit properly. Local search strategies in addition to existing steps play important role to improve exploitation. In order to improve exploitation here a local search inspired by the nature of hyperbolic spiral introduced in ABC. The purposed variant used with ABC, Best-so-far ABC and Gbest ABC. Outcomes proved that hybrid of these algorithms with hyperbolic search gives good results with higher accuracy and reliability.

Design and Development of a Modified Artificial Bee Colony Approach

2020 ◽  
pp. 365-381
Author(s):  
Saad T. Alharbi
Keyword(s):  
Local Search ◽  
Real World ◽  
Artificial Bee Colony ◽  
Search Algorithms ◽  
Traveling Salesman ◽  
Benchmark Problem ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Benchmark Instances ◽  
The Traveling Salesman Problem

The traveling thief problem (TTP) is a benchmark problem that consists of two well-known problems, the traveling salesman problem (TSP) and the knapsack problem (KP). It was defined to imitate complex real-world applications that comprise different interdependent sub-problems. Various approaches were proposed in the literature to solve such a problem. These approaches mostly focus on local search algorithms, heuristics methods and evolutionary approaches. In addition, some of these approaches concentrated on solving the problem by considering each sub-problem independently. Thus far, limited approaches were proposed to solve the problem using swarm intelligence. In this article, the authors introduce a modified artificial bees colony (ABC) algorithm that addresses the TTP in an interdependent manner. The performance of this approach was compared with various recent approaches in the literature using different benchmark instances. The obtained results demonstrated that it is competitive with the state-of-the-art approaches, especially on small and medium instances.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

scholarly journals Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

2021 ◽  
Author(s):  
Ali Ebrahimnejad ◽  
Mohammad Enayattabr ◽  
Homayun Motameni ◽  
Harish Garg
Keyword(s):  
Shortest Path ◽  
Artificial Bee Colony ◽  
Fuzzy Numbers ◽  
Pso Algorithm ◽  
Convergence Time ◽  
Bee Colony ◽  
Different Types ◽  
Implementation Time ◽  
Fuzzy Network ◽  
Interval Valued

AbstractIn recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued fuzzy numbers using alpha cuts. Then, an extended distance function is introduced for comparing the path weights. Finally, we intend to use a modified artificial bee colony (MABC) algorithm to find the interval-valued membership function of SP in such mixed interval-valued fuzzy network. The proposed algorithm is illustrated via two applications of SP problems in wireless sensor networks and then the results are compared with those derived from genetic and particle swarm optimization (PSO) algorithms, based on three indexes convergence iteration, convergence time and run time. The obtained results confirm that the MABC algorithm has less convergence iteration, convergence time and implementation time compared to GA and PSO algorithm.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

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