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A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期197卷

作者： Ramachandran, Murugan Mirjalili, Seyedali Ramalingam, Mohan Malli Gnanakkan, Christober Asir Rajan Charles Parvathysankar, Deiva Sundari Sundaram, Arunachalam KCG Coll Technol Dept Elect & Elect Engn Chennai 600097 Tamil Nadu India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld Australia Meenakshi Coll Engn Dept Elect & Elect Engn Chennai 600078 Tamil Nadu India Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry 605014 India Jubail Ind Coll Dept Elect & Elect Engn Technol Al Jubail 31961 Jubail Ind City 31961 Saudi Arabia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking-based Crow Search algorithm (FRCSA) is amalgamated with modified artificial bee colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual's best solution (p(best)) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

关键词： Combined heat and power economic dispatch Crow search algorithm artificial bee colony algorithm Adaptive ranking selection Fuzzy logic Sine-Cosine

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Modified ABC-WOA algorithm under Partial Shading Condition for MPPT in PV System

Modified ABC-WOA Algorithm under Partial Shading Condition f...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Mahesh Singh Asshutosh Upadhyay Shimpy Ralhan Shashank Singh Department of EEE SSGI SSTC (FET) Junwani Bhilai C.G. INDIA

The PV system is power production technologies developed by renewable energy resources which offers the benefits of power scaling, ease of installation, requires low maintenance and modularity. Solar photovoltaic (PV) systems are semiconductor technologies that change solar energy into direct current (DC) electricity via the exchange of electrons. A “Maximum Point Tracking” (MPPT) technique is derived to increase PV array's output power under all circumstances by monitoring the maximum power. The point of maximum power was tracked by various MPPT algorithms including the incremental conductance, perturb and observe (P&O), constant current, constant voltage, and parasitic capacitance methods etc. Despite being a straightforward technique, the perturb and monitor algorithm tracks incorrectly when the weather suddenly changes and oscillates around the point of maximum power (MPP), which can be avoided by utilizing the hybrid ABC-WOA algorithm. Therefore, to overcome this problem, an MPPT approach that is built on the hybrid ABC-WOA algorithm was performed associated to that of Perturbation and observation (P&O) as benchmarks. The ABC-WOA algorithm has ability to efficiently decrease the start-up time, minimizes the steady-state-run-yield oscillation of power following step change in an irradiance and enhances output efficiency. The recommended algorithm may easily handle partly shadowed PV arrays; lower power losses brought on by improperly recognizing the local position, and increase the effectiveness of PV power production.

关键词： Photovoltaic systems Renewable energy sources Perturbation methods Production Solar energy artificial bee colony algorithm Steady-state

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A Discrete artificial bee colony algorithm for Minimizing the Total Flow Time in the Blocking Flow Shop Scheduling

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Chinese Journal of Chemical Engineering 2012年第6期20卷 1067-1073页

作者：邓冠龙徐震浩顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.

关键词： blocking flow shop scheduling artificial bee colony algorithm total flow time

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Optimization of Association Rule Mining Using Hybridized artificial bee colony (ABC) with BAT algorithm 7

Optimization of Association Rule Mining Using Hybridized Art...

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7th IEEE International Advance Computing Conference (IACC)

作者： Neelima, S. Satyanarayana, N. Murthy, P. Krishna JNTUH Dept CSE Hyderabad Andhra Pradesh India Nagole Inst Technol & Sci Dept CSE Hyderabad Andhra Pradesh India Swarna Bharathi Inst Sci & Technol Khammam India

ISBN: (纸本)9781509015603

One of the major tasks of data mining is association rule mining, which is used for finding the interesting relationships among the items in itemsets of huge database. Aproiri is the familiar algorithm of association rule mining for generating frequent itemsets. Apriori uses minimum support threshold to find frequent items. In this paper, an algorithm called hybridization of ABC with BAT algorithm is proposed which is used for optimization of association rules. Instead of onlooker bee phase of ABC, random walk of BAT is used in order to increase the exploration. Hybridized ABC with BAT algorithm is applied on the rules generated from apriori algorithm, for optimizing association rules. The experiments are performed on datasets taken from UCI repository which show the proposed work performance and proposed methodology can effectively optimize association rules when compared to the existing algorithms. In the paper, we also proved that the rules generated using proposed work are simple and comprehensible.

关键词： Apriori algorithm artificial bee colony algorithm Association Rule Mining BAT algorithm

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Using an Evolutionary Fuzzy Neural Network for Sensor-based Wall-following Control of a Mobile Robot

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SENSORS AND MATERIALS 2020年第11期32卷 3627-3645页

作者： Chen, Cheng-Hung Jeng, Shiou-Yun Lin, Cheng-Jian Natl Formosa Univ Dept Elect Engn Huwei Township 632 Yunlin Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan Natl Taichung Univ Sci & Technol Coll Intelligence Taichung 404 Taiwan

We propose an efficient evolutionary fuzzy neural network (EFNN) for mobile robot control. The proposed EFNN combines a fuzzy neural network (FNN) and an improved artificial bee colony (IABC) algorithm to implement the wall-following control of a mobile robot. To evaluate the wall-following control performance of the FNN, an efficient fitness function is defined. The three control factors (CFs) in the fitness function are the maintenance of the robot-wall distance, the avoidance of robot-wall collision, and the successful movement of the robot along a wall to travel around a stadium. The traditional ABC emulates the intelligent foraging behavior of honey bee swarms, but this algorithm performs favorably at exploration and poorly at exploitation. Therefore, the proposed IABC algorithm uses mutation strategies to balance exploration and exploitation. Furthermore, a new reward-based roulette wheel selection (RRWS) mechanism is adopted to obtain a more favorable solution during the learning process. Experimental results demonstrate that the proposed IABC obtains a smaller root mean square error (RMSE) than other methods in wall-following control.

关键词： mobile robot control fuzzy neural network artificial bee colony algorithm wall-following control differential evolution

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Multi-objective traveling salesman problem: an ABC approach

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APPLIED INTELLIGENCE 2020年第11期50卷 3942-3960页

作者： Khan, Indadul Maiti, Manas Kumar Basuli, Krishnendu Chandrakon Vidyasagar Mahavidyalaya Dept Comp Sci Chandrakona 721201 W Bengal India Mahishadal Raj Coll Dept Math Garh Kamalpur 721628 W Bengal India West Bengal State Univ Dept Comp Sci Kolkata 700126 W Bengal India

Using the concept of swap operation and swap sequence on the sequence of paths of a Traveling Salesman Problem(TSP) artificial bee colony (ABC) algorithm is modified to solve multi-objective TSP. The fitness of a solution is determined using a rule following the dominance property of a multi-objective optimization problem. This fitness is used for the selection process of the onlooker bee phase of the algorithm. A set of rules is used to improve the solutions in each phase of the algorithm. Rules are selected according to their performance using the roulette wheel selection process. At the end of each iteration, the parent solution set and the solution sets after each phase of the ABC algorithm are combined to select a new solution set for the next iteration. The combined solution set is divided into different non-dominated fronts and then a new solution set, having cardinality of parent solution set, is selected from the upper-level non-dominated fronts. When some solutions are required to select from a particular front then crowding distances between the solutions of the front are measured and the isolated solutions are selected for the preservation of diversity. Different standard performance metrics are used to test the performance of the proposed approach. Different sizes standard benchmark test problems from TSPLIB are used for the purpose. Test results show that the proposed approach is efficient enough to solve multi-objective TSP.

关键词： Multi-objective traveling salesmen problem artificial bee colony algorithm Swap operation Pareto optimal solution Performance metric

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Minimizing the sum of makespan on multi-agent single-machine scheduling with release dates

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 100996-100996页

作者： Wang, Xinyue Ren, Tao Bai, Danyu Ezeh, Chinenye Zhang, Haodong Dong, Zhuoran Northeastern Univ Software Coll Shenyang 110819 Peoples R China Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Michael Okpara Univ Agr Dept Comp Engn Umudike 440109 Nigeria

Competitive scheduling problems occur in integrated-services packet-switched networks, in which different applications compete for the same resource. Requests of each application arrive over time and contain multiple packets. This process can be described as a multi-agent single-machine scheduling (MSS) problem with release dates, where several packets (jobs) share a common network (processor) but are maintained by several competitive applications (agents) that optimize their criteria. The objective is to minimize the sum of makespans belonging to several agents individually. The NP-hardness of the model indicates that it cannot be optimally solved in polynomial time. For small-scale instances, an effective branch and bound (B&B) algorithm integrated with an elaborately designed pruning rule and a lower bound is developed to achieve exact solutions. Given the quality of service requirements and the periodical maintenance of networks, this problem is generalized to the weighted and periodic maintenance versions, respectively. For the weighted version, a hybrid discrete artificial bee colony (HDABC) algorithm integrated with effective improvement strategies is developed to achieve satisfactory solutions for medium-scale instances. For the periodic maintenance version, a deep reinforcement learning (DRL) method is introduced to obtain high-quality solutions in dynamic situation. Results of computational evaluation demonstrate the effectiveness of the proposed methods

关键词： Multi-agent scheduling Release date Branch and bound artificial bee colony algorithm Heuristic Deep Reinforcement learning

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A Novel Service Composition algorithm for Cloud-Based Manufacturing Environment

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IEEE ACCESS 2020年 8卷 39148-39164页

作者： Zhu, Linan Li, Penghang Shen, Guojiang Liu, Zhi Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Cloud Manufacturing Service Composition (CMSC) is the key issue and taking an important role in solving the interconnection and interoperability of resources and services for Cloud Manufacturing (CMfg). CMSC is a typical kind of NP-hard problems with the characteristics of dynamic and uncertainty. Solving large scale CMSC problem by using the traditional methods might be not efficient because of the massive complex resources and large-scale searching space. To overcome this shortcoming, a novel artificial bee colony algorithm named Multiple Improvement Strategies based artificial bee colony algorithm (MISABC) is proposed. MISABC improves the performance of classical ABC algorithm through several strategies such as (a) differential evolution strategy (DES), (b) oscillation strategy with classical trigonometric factor (TFOS), (c) different dimensional variation learning strategy (DDVLS), (d) Gaussian distribution strategy (GDS). Meanwhile, to address the CMfg scenario, we also propose a manufacturing service composition scheme named as Multi-Module Subtasks Collaborative Execution for Cloud Manufacturing Service Composition (MMSCE-CMSC). Eight benchmark functions with different characteristics, a comparison study with existed improved ABC algorithms and a case study are used to validate the performance of the algorithm. The results demonstrate the effectiveness of the proposed method for addressing complex CMSC problem in CMfg.

关键词： Manufacturing Cloud computing Task analysis Optimization Dynamic scheduling Quality of service Cloud manufacturing cloud manufacturing service composition multi-module subtasks artificial bee colony algorithm

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Regression-Based Neuro-Fuzzy Network Trained by ABC algorithm for High-Density Impulse Noise Elimination

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第6期28卷 1084-1095页

作者： Caliskan, Abdullah Cil, Zeynel Abidin Badem, Hasan Karaboga, Dervis Iskenderun Tech Univ Dept Biomed Engn TR-31200 Antakya Turkey Izmir Democracy Univ Dept Ind Engn TR-35140 Izmir Turkey Kahramanmaras Sutcu Imam Univ Dept Comp Engn TR-46050 Kahramanmaras Turkey Erciyes Univ Dept Comp Engn TR-38030 Kayseri Turkey King Abdulaziz Univ Dept Informat Syst Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

Salt and pepper (SAP) noise elimination is a crucial step for further image processing and pattern recognition applications. The main aim of this article is to propose a novel SAP noise elimination method which employs a regression-based neuro-fuzzy network for highly corrupted gray scale and color images. In the proposed method, multiple neuro-fuzzy filters trained with artificial bee colony algorithm is combined with a decision tree algorithm. The performance of the proposed filter is compared with a number of well known methods with respect to popular metrics including, structural similarity index, peak signal-to-noise ratio, and correlation on well known test images. The results reveal that the proposed filter has superior performance in terms of all comparison metrics.

关键词： Noise measurement Microsoft Windows Noise reduction Fuzzy neural networks artificial bee colony algorithm Decision trees Image edge detection artificial bee colony (ABC) decision tree (DT) impulse noise neuro-fuzzy (NF) network

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ABC algorithm-Based Subblocks Set Design for OFDM With All Index Modulation

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IEEE COMMUNICATIONS LETTERS 2020年第12期24卷 2926-2930页

作者： Yildirim, Mahmut Bozok Univ Dept Elect Elect Engn TR-66900 Yozgat Turkey

In this letter, we propose a low complexity artificial bee colony (ABC) algorithm based subblocks set design to maximize the diversity gain of orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), which is inspired by OFDM with index modulation (OFDM-IM). Unlike the conventional OFDM-IM, where constellation points of the M-ary signals are fixed, ABC aided OFDM-AIM (ABC-OFDM-AIM) scheme has flexible subcarrier constellation points to enhance the diversity gain, which ensures better BER performance. Also, a parallel ABC (PABC) algorithm, which has a more powerful search strategy than the ABC algorithm, is proposed (PABC-OFDM-AIM) to further enhance the diversity gain of the OFDM-AIM. Performances of the proposed schemes have been tested according to the average bit error probability (ABEP), search complexity, and BER performance criteria. Simulation results show that the proposed schemes outperform other OFDM-IM based schemes, especially in the improvement of BER.

关键词： Silicon OFDM Diversity methods Indexes Table lookup Modulation artificial bee colony algorithm OFDM all index modulation pairwise error probability (PEP) average bit error probability (ABEP)

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