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Altshuler Y. Recent Developments in the Theory and Applicability of Swarm Search. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050710. [PMID: 37238465 DOI: 10.3390/e25050710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/17/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023]
Abstract
Swarm intelligence (SI) is a collective behaviour exhibited by groups of simple agents, such as ants, bees, and birds, which can achieve complex tasks that would be difficult or impossible for a single individual [...].
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Heuristic smoothing ant colony optimization with differential information for the traveling salesman problem. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2022.109943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT. JOURNAL OF CLOUD COMPUTING 2022. [DOI: 10.1186/s13677-022-00298-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
AbstractIn recent years, with the development of Unmanned Aerial Vehicle (UAV) and Cloud Internet-of-Things (Cloud IoT) technology, data collection using UAVs has become a new technology hotspot for many Cloud IoT applications. Due to constraints such as the limited power life, weak computing power of UAV and no-fly zones restrictions in the environment, it is necessary to use cloud server with powerful computing power in the Internet of Things to plan the path for UAV. This paper proposes a coverage path planning algorithm called Parallel Self-Adaptive Ant Colony Optimization Algorithm (PSAACO). In the proposed algorithm, we apply grid technique to map the area, adopt inversion and insertion operators to modify paths, use self-adaptive parameter setting to tune the pattern, and employ parallel computing to improve performance. This work also addresses an additional challenge of using the dynamic Floyd algorithm to avoid no-fly zones. The proposal is extensively evaluated. Some experiments show that the performance of the PSAACO algorithm is significantly improved by using parallel computing and self-adaptive parameter configuration. Especially, the algorithm has greater advantages when the areas are large or the no-fly zones are complex. Other experiments, in comparison with other algorithms and existing works, show that the path planned by PSAACO has the least energy consumption and the shortest completion time.
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Kazancoglu I, Ozbiltekin-Pala M, Mangla SK, Kumar A, Kazancoglu Y. Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19. ANNALS OF OPERATIONS RESEARCH 2022; 322:217-240. [PMID: 35789688 PMCID: PMC9243821 DOI: 10.1007/s10479-022-04775-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
In rapidly changing business conditions, it has become extremely important to ensure the sustainability of supply chains and further improve the resiliency to those events, such as COVID-19, that can cause unexpected disruptions in the value supply chain. Although globalized supply chains have already been criticized for lack of control over sustainability and resilience of supply chain operations, these issues have become more prevalent in the uncertain environment driven by COVID-19. The use of emerging technologies such as blockchain, Industry 4.0 analytics model and artificial intelligence driven methods are aimed at increasing the sustainability and resilience of supply chains, especially in an uncertain environment. In this context, this research aims to identify the problematic areas encountered in building a resilient and sustainable supply chain in the pre-COVID-19 era and during COVID-19, and to offer solutions to those problematic areas tackled by an appropriate emerging technology. This research has been contextualized in the automotive industry; this industry has a complex supply chain structure and is one of the sectors most affected by COVID-19. Based on the findings, the most important problematic areas encountered in SSCM pre-COVID-19 are determined as supply chain traceability, demand planning and production management as well as purchasing process planning based on cause and effect groups. The most important issues to be addressed during COVID-19 are top management support, purchasing process planning and supply chain traceability, respectively.
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Affiliation(s)
- Ipek Kazancoglu
- Department of Business Administration, Faculty of Economics and Administrative Sciences, Ege University, 35100 İzmir, Turkey
| | | | - Sachin Kumar Mangla
- Research Centre on Digital Circular Economy for Sustainable Development Goals (DCE-SDG), Jindal Global Business School, O P Jindal University, Sonepat, India
| | - Ajay Kumar
- AIM Research Center on AI in Value Creation, EMLYON Business School, Écully, France
| | - Yigit Kazancoglu
- Department of Logistics Management, Yasar University, 35100 İzmir, Turkey
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A novel hybrid GWO-PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions. Sci Rep 2022; 12:10637. [PMID: 35739302 PMCID: PMC9226354 DOI: 10.1038/s41598-022-14733-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/28/2022] [Indexed: 11/08/2022] Open
Abstract
One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO–PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO–PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.
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Abdenebaoui L, Kreowski HJ, Kuske S. A Graph-Transformational Approach to Swarm Computation. ENTROPY 2021; 23:e23040453. [PMID: 33921251 PMCID: PMC8070391 DOI: 10.3390/e23040453] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/22/2021] [Accepted: 03/31/2021] [Indexed: 12/04/2022]
Abstract
In this paper, we propose a graph-transformational approach to swarm computation that is flexible enough to cover various existing notions of swarms and swarm computation, and it provides a mathematical basis for the analysis of swarms with respect to their correct behavior and efficiency. A graph transformational swarm consists of members of some kinds. They are modeled by graph transformation units providing rules and control conditions to specify the capability of members and kinds. The swarm members act on an environment—represented by a graph—by applying their rules in parallel. Moreover, a swarm has a cooperation condition to coordinate the simultaneous actions of the swarm members and two graph class expressions to specify the initial environments on one hand and to fix the goal on the other hand. Semantically, a swarm runs from an initial environment to one that fulfills the goal by a sequence of simultaneous actions of all its members. As main results, we show that cellular automata and particle swarms can be simulated by graph-transformational swarms. Moreover, we give an illustrative example of a simple ant colony the ants of which forage for food choosing their tracks randomly based on pheromone trails.
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Affiliation(s)
- Larbi Abdenebaoui
- OFFIS—Institute for Information Technology, Escherweg 2, 26122 Oldenburg, Germany;
| | - Hans-Jörg Kreowski
- Department of Computer Science, University of Bremen, P.O. Box 330440, D-28334 Bremen, Germany;
- Correspondence:
| | - Sabine Kuske
- Department of Computer Science, University of Bremen, P.O. Box 330440, D-28334 Bremen, Germany;
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Baniata H, Anaqreh A, Kertesz A. PF-BTS: A Privacy-Aware Fog-enhanced Blockchain-assisted task scheduling. Inf Process Manag 2021. [DOI: 10.1016/j.ipm.2020.102393] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu WL, Gong YJ, Chen WN, Zhang J. EvoTSC: An evolutionary computation-based traffic signal controller for large-scale urban transportation networks. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Abstract
This paper introduces a method for the distribution of any and all population-based metaheuristics. It improves on the naive approach, independent multiple runs, while adding negligible overhead. Existing methods that coordinate instances across a cluster typically require some compromise of more complex design, higher communication loads, and solution propagation rate, requiring more work to develop and more resources to run. The aim of the new method is not to achieve state-of-the-art results, but rather to provide a better baseline method than multiple independent runs. The main concept of the method is that one of the instances receives updates with the current best solution of all other instances. This work describes the general approach and its particularization to both genetic algorithms and ant colony optimization for solving Traveling Salesman Problems (TSPs). It also includes extensive tests on the TSPLIB benchmark problems of resulting quality of the solutions and anytime performance (solution quality versus time to reach it). These tests show that the new method yields better solutions for about two thirds of the problems and equivalent solutions in the remaining third, and consistently exhibits better anytime performance.
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Telikani A, Gandomi AH, Shahbahrami A. A survey of evolutionary computation for association rule mining. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.02.073] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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A Proposal for the Organisational Measure in Intelligent Systems. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The collaboration within organisations and among organisations is a fundamental concept in the attainment of the overall objectives pursued by an enterprise network in human companies. Swarm systems are intelligent systems that show collaboration within the system; moreover, some models, such as multiple ant colonies, show the collaboration of several systems to achieve a global goal. The collaboration in this type of system optimises the achievement of the overall objectives as in an enterprise network in human organisations. Being able to measure this collaboration allows establishing a relationship between the improvement in the results of the system and the degree of collaboration, both at the level of specialisation of each element of the system and the systems as a whole. The performance of a swarm system depends on the number of members in many cases, so that if we can establish a measure of specialisation and collaboration, we could tipify and classify these systems in terms of the efficiency and the realiability to perform different tasks.
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Starzec M, Starzec G, Byrski A, Turek W, Kisiel-Dorohinicki M. Distributed ant system for difficult transport problems. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-179344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | | | | | - Wojciech Turek
- AGH University of Science and Technology, Krakow, Poland
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Siemiński A, Kopel M. Solving dynamic TSP by parallel and adaptive ant colony communities. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-179366] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Andrzej Siemiński
- Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland
| | - Marek Kopel
- Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland
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Birjali M, Beni-Hssane A, Erritali M. A novel adaptive e-learning model based on Big Data by using competence-based knowledge and social learner activities. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.04.030] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Cellular matrix model for parallel combinatorial optimization algorithms in Euclidean plane. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Strąk Ł, Skinderowicz R, Boryczka U. Adjustability of a discrete particle swarm optimization for the dynamic TSP. Soft comput 2017. [DOI: 10.1007/s00500-017-2738-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Siemiński A, Kopel M. Comparing efficiency of ACO parallel implementations. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-169135] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Pedemonte M, Luna F, Alba E. A Systolic Genetic Search for reducing the execution cost of regression testing. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.07.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Djenić A, Radojičić N, Marić M, Mladenović M. Parallel VNS for Bus Terminal Location Problem. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.02.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Liang Z, Sun J, Lin Q, Du Z, Chen J, Ming Z. A novel multiple rule sets data classification algorithm based on ant colony algorithm. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.10.046] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Gong YJ, Chen WN, Zhan ZH, Zhang J, Li Y, Zhang Q, Li JJ. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.04.061] [Citation(s) in RCA: 247] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.03.047] [Citation(s) in RCA: 208] [Impact Index Per Article: 23.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Zhang Z, Gao C, Liu Y, Qian T. A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model. BIOINSPIRATION & BIOMIMETICS 2014; 9:036006. [PMID: 24613939 DOI: 10.1088/1748-3182/9/3/036006] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.
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Affiliation(s)
- Zili Zhang
- School of Computer and Information Science, Southwest University, Chongqing 400715, People's Republic of China. School of Information Technology, Deakin University, 3217, Australia
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Tsai CW, Tseng SP, Yang CS, Chiang MC. PREACO: A fast ant colony optimization for codebook generation. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2013.01.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal. Soft comput 2013. [DOI: 10.1007/s00500-013-0993-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Cheng B, Wang Q, Yang S, Hu X. An improved ant colony optimization for scheduling identical parallel batching machines with arbitrary job sizes. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2012.10.021] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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