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6th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Arsuaga-Rios, Maria Vega-Rodriguez, Miguel A. CERN European Org Nucl Res IT Dept CH-1211 Geneva Switzerland Univ Extremadura Dept Technol Comp & Commun Escuela Politecn ARCO Res Grp Caceres Spain

ISBN: (纸本)9781479959372

Green Computing also known as Green IT is becoming a hot topic in the computational field during these last years. Green Computing consists of enabling organizations to make a more rational and efficient use of their technological resources and reduce costs while adopting technologies and working methods more respectful of the environment. Execution time and energy consumption are also conflicting objectives, because faster resources frequently imply higher energy consumptions. In this paper, we optimize both: execution time and energy consumption to resolve the task scheduling problem in Grid environments. MOABC is a swarm algorithm inspired in the bees behaviour and it is compared with MO-FA which is other swarm algorithm inspired in the fireflies behaviour. These algorithms are also compared with the well-known NSGA-II to evaluate their multiobjective properties. Moreover, the best algorithm, MOABC, is compared with MOHEFT, the most popular algorithm for workflow scheduling and with two real grid schedulers as WMS or DBC. The results obtained point out MOABC as the best approach in all the cases studied.

关键词： task scheduling energy optimization multiobjective optimization distributed systems swarm algorithms

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Modified Shuffled Multi-Population Bat Algorithm 7

Modified Shuffled Multi-Population Bat Algorithm

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7th International Conference on Computing, Communications and Informatics (ICACCI)

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci Delhi India Univ Delhi Dyal Singh Coll Delhi India

ISBN: (纸本)9781538653142

Shuffled Multi-Population Bat algorithm (SMPBat) is a recently proposed hybrid variant of bat algorithm. It incorporates the strengths of two recent variants of bat algorithm-Enhanced Shuffled Bat Algorithm and Bat algorithm with Ring Master-Slave strategy. SMPBat hybridizes the sub-population generation and manipulation mechanism of the two algorithms to device an enhanced variant of BA. There are multiple parameters controlling the flow of execution of SMPBat. These parameters are set at the beginning of the execution of the algorithm. This paper proposes incorporation of multiparameter setting into SMPBat, where different sub-populations work with different sets of parameter values. Additionally, the impact of a refined search mechanism is also studied. The proposed variants are tested over 20 benchmark functions and a real-world optimization problem. Results establish the robustness of the proposed work.

关键词： Bat algorithm Multi-Population algorithms Multi-Parameter swarm algorithms Numerical optimization

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On Evolutionary Dynamics Modeled by Ant Algorithm 8

On Evolutionary Dynamics Modeled by Ant Algorithm

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8th International Conference on Intelligent Networking and Collaborative Systems (INCoS)

作者： Zelinka, Ivan Tomaszek, Lukas Kojecky, Lumir VSB Tech Univ Ostrava Dept Comp Sci FEI Tr 17 Listopadu 15 Ostrava Czech Republic

ISBN: (纸本)9781509041237

In this paper we are continuing in our research to show mutual intersection of two different areas of research: complex network and evolutionary computation. This research parer is focused on possibility to convert run of evolution algorithm to a complex network inspired by ants. Such network can then be analyzed to get useful information about algorithm dynamics. In this paper we focused on one global network property, average network strength. This property is described and used for different types of cost functions and different representations of network conversion.

关键词： Complex networks Evolutionary algorithms SOMA algorithm swarm algorithms

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Comparative Analysis of Chaotic Variant of Firefly Algorithm, Flower Pollination Algorithm and Dragonfly Algorithm for High Dimension Non-linear Test Functions 1

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9th International Conference on Innovations in Bio- Inspired Computing and Applications (IBICA) / 7th World Congress on Information and Communication Technologies (WICT)

作者： Singh, Amrit Pal Kaur, Arvinder GGSIPU Bharati Vidyapeeths Coll Engn New Delhi India GGSIPU Univ Sch Informat & Commun Technol New Delhi India

ISBN: (数字)9783030166816

ISBN: (纸本)9783030166816;9783030166809

Non-linear test functions are NP-Class problems. To solve them, swarm algorithms (SA) have been used in last two decades very effectively. In this work, three swarm based algorithms (i.e. Firefly Algorithm (FFA);Flower Pollination Algorithm (FPA) and Dragonfly Algorithm (DA)) have been used. Chaos is familiarized with swarm algorithm to improve their performance. As per our knowledge, most of the studies have applied chaos on one standard SA and compared it with other standard algorithm(s). No comparison has been shown among the chaotic variant of different algorithms. Comparison of Chaotic variants of FFA, FPA & DA with their standard algorithms has been performed using four high dimensions non-linear test functions on the basis of Mean fitness (i.e. P1) and convergence rate (i.e. P2). The results indicate that chaotic variant has performed better than standard and FFA evaluates best fitness for multi-modal function (i.e. f3 and f4).

关键词： swarm algorithms Firefly Algorithm Flower Pollination Algorithm Dragonfly Algorithm Chaos theory Non-linear test functions

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Shuffled Multi-Population Bat Algorithm (SMPBat)

Shuffled Multi-Population Bat Algorithm (SMPBat)

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci Delhi India Univ Delhi Dyal Singh Coll Delhi India

ISBN: (纸本)9781509063673

Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimisation problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different hybrids of BA. This paper proposes Shuffled Multi Population Bat algorithm (SMPBat), a hybrid between two recently proposed variants of BA:- Enhanced Shuffled Bat algorithm (EShBAT) and Bat algorithm with Ring Master-Slave strategy (BatRM-S). BatRM-S is a multi-population variant of BA which partitions it's population according to a combination of the ring and master-slave strategies. EShBAT incorporates shuffling into BA. The proposed algorithm, SMPBat combines the population partitioning strategies of these two algorithms to enhance the exploitation and exploration capabilities of BA. The evolution strategy of SMPBat also strives to retain the improved solutions. The standard BA replaces a solution with a new solution around the best. However, in this process, the information gained by that solution so far is completely lost. SMPBat changes this exploitation technique used by BA. SMPBat is compared to BatRM-S, EShBAT and BA, over 30 well-known optimisation functions. Results establish SMPBat as a significant improvement over BA, EShBAT and BatRM-S.

关键词： Bat algorithm Master-Slave strategy Multi Population algorithms swarm algorithms Numerical optimisation

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Hybrid Black Widow Optimization and Variable Neighborhood Descent Algorithm for Traveling Salesman Problem

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2021年第5期6卷 32-43页

作者： Jabbar, Ayad Mohammed Ku-Mahamud, Ku Ruhana Computer Science Department Shatt Al-Arab University College Iraq School of Computing Universiti Utara Malaysia Malaysia

Local search algorithms in general are better than population-based algorithms in the terms of exploitation capability in finding more local regions in the search space which provide more ability to explore search space in finding global regions. Black widow optimization (BWO) algorithm is one of the best population-based algorithms which was proposed in 2020 to solve engineering optimization problems. However, this algorithm has a limitation in the exploitation of search space and reactivate a search when stagnation occurs during the algorithm run. Thus, deep search and effectively exploring the search space are not possible during the algorithm run. To overcome these drawbacks, this study proposes two modifications to the BWO algorithm. The first modification is the integration of variable neighborhood descent used to enhance the exploitation process in finding more local regions in the neighborhood during the algorithm run. The second modification focuses on the reactive search process by integrating a new convergence indicator for the algorithm during the algorithm run and online reactive search process. Two benchmark datasets were used to evaluate the proposed modification. The minimum tour distance provided by each algorithm has been used as the performance metric in determining the credibility of the hybrid BWO algorithm and results have been compared with best-known algorithms include African buffalo optimization (ABO), ant colony optimization (ACO), artificial bee colony (ABC), particle swarm optimization (PSO) and a hybrid algorithm consisting of harmony search, particle swarm and ACO (HPSACO). The hybrid BWO algorithm has produced better minimum tour distance compared to ABO, ACO, ABC, PSO and HPSACO algorithms which demonstrate that the hybrid BWO can be applied to solve several optimization problems including vehicle routing problem, classification and clustering. © 2021. All Rights Reserved.

关键词： Exploitation Exploration Local search Neighborhood search swarm algorithms Traveling salesman problem

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Performance of the Bison Algorithm on Benchmark IEEE CEC 2017 7th

Performance of the Bison Algorithm on Benchmark IEEE CEC 201...

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7th Computer Science On-Line Conference (CSOC)

作者： Kazikova, Anezka Pluhacek, Michal Senkerik, Roman Tomas Bata Univ Zlin Fac Appl Informat TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9783319911892

This paper studies the performance of a newly developed optimization algorithm inspired by the behavior of bison herds: the Bison Algorithm. The algorithm divides its population into two groups. The exploiting group simulates the swarming behavior of bison herds endangered by predators. The exploring group systematically runs through the search space in order to avoid local optima. At the beginning of the paper, the Bison Algorithm is introduced. Then the performance of the algorithm is compared to the Particle swarm Optimization and the Cuckoo Search on the set of 30 benchmark functions of IEEE CEC 2017. Finally, the outcome of the experiments is discussed.

关键词： Bison Algorithm Bison Optimization swarm algorithms Metaheuristic

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Regarding the Behavior of Bison Runners Within the Bison Algorithm

Regarding the Behavior of Bison Runners Within the Bison Alg...

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作者： Kazikova, Anezka Brno University of Technology

This paper proposes a modification of the Bison Algorithm’s running technique, which allows the running group to exploit the areas of discovered promising solutions. It also provides a closer examination of the successful running behavior and its impact on the overall optimization process. The new algorithm is then compared to other optimization algorithms on the IEEE CEC 2017 benchmark solving continuous minimization problems.

关键词： bison algorithm bison seeker algorithm optimization swarm algorithms

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On the Leader Selection in the Self-Organizing Migrating Algorithm

On the Leader Selection in the Self-Organizing Migrating Alg...

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作者： Tomaszek, Lukas Brno University of Technology

In this article, a novel leader selection strategy for the self-organizing migrating algorithm is introduced. This strategy replaces original AllToOne and AllToRand strategies. It is shown and statistically tested, that the new strategy outperforms the original ones. All the experiments were conducted on well known CEC 2014 benchmark functions according to the CEC competition rules and reported here.

关键词： self-organizing migrating algorithm CEC 2014 benchmark AllToNBest strategy swarm algorithms

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Why Tuning the Control Parameters of Metaheuristic algorithms Is So Important for Fair Comparison?

Why Tuning the Control Parameters of Metaheuristic Algorithm...

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作者： Kazikova, Anezka Brno University of Technology

Although metaheuristic optimization has become a common practice, new bio-inspired algorithms often suffer from a priori ill reputation. One of the reasons is a common bad practice in metaheuristic proposals. It is essential to pay attention to the quality of conducted experiments, especially when comparing several algorithms among themselves. The comparisons should be fair and unbiased. This paper points to the importance of proper initial parameter configurations of the compared algorithms. We highlight the performance differences with several popular and recommended parameter configurations. Even though the parameter selection was mostly based on comprehensive tuning experiments, the algorithms’ performance was surprisingly inconsistent, given various parameter settings. Based on the presented evidence, we conclude that paying attention to the metaheuristic algorithm’s parameter tuning should be an integral part of the development and testing processes.

关键词： Parameter tuning metaheuristics comparison swarm algorithms configuration particle swarm optimization

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