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Hunting optimization: An New Framework for single objective optimization Problems

IEEE ACCESS 2019年 7卷 31305-31320页

作者： Zhao, Zhenchong Wang, Xiaodan Wu, Chongming Lei, Lei Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China Xijing Univ Coll Business Xian 710123 Shaanxi Peoples R China

Swarm intelligence algorithms play vital roles in objective optimization problems. To solve diverse and increasingly complicated problems, a newalgorithm is always desired. This paper proposes a new optimization algorithm named hunting optimization based on human hunting activities. The population has consisted of huntsmen and hunting dogs. Each of them represents a feasible solution. In the evolution process, each huntsman shrinks its hunting ground with an adaptive reduction factor to concentrate on searching the most promising area. Then, each huntsman uses its dogs to search its local hunting ground and updates its position to a more promising place by its own searching results as well as the results of other huntsmen. At the same time, to further balance the exploration and exploitation, huntsmen with the least prey will be eliminated and their dogs will be distributed to others. Congestion detection is also applied to avoid getting stuck at a local optimum. The experimental results on 12 benchmark functions and CEC2013 test suites compared with 12 state-of-the-art algorithms demonstrate the effectiveness of the proposed method.

关键词： Evolutionary computation single objective optimization swarm intelligence hunting optimization

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Adaptive representation for single objective optimization

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SOFT COMPUTING 2005年第8期9卷 594-605页

作者： Grosan, C Oltean, M Univ Babes Bolyai Fac Math & Comp Sci Dept Comp Sci R-3400 Cluj Napoca Romania

A new technique called Adaptive Representation Evolutionary Algorithm (AREA) is proposed in this paper. AREA involves dynamic alphabets for encoding solutions. The proposed adaptive representation is more compact than binary representation. Genetic operators are usually more aggressive when higher alphabets are used. Therefore the proposed encoding ensures an efficient exploration of the search space. This technique may be used for single and multiobjective optimization. We treat the case of single objective optimization problems in this paper. Despite its simplicity the AREA method is able to generate a population converging towards optimal solutions. Numerical experiments indicate that the AREA technique performs better than other single objective evolutionary algorithms on the considered test functions.

关键词： evolution strategy single objective optimization adaptive representation higher alphabets encoding

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Real Parameter single objective optimization using Self-Adaptive Differential Evolution Algorithm with more Strategies

Real Parameter Single Objective Optimization using Self-Adap...

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IEEE Congress on Evolutionary Computation

作者： Brest, Janez Boskovic, Borko Zamuda, Ales Fister, Iztok Mezura-Montes, Efren Univ Maribor Fac Elect Engn & Comp Sci Inst Comp Sci Smetanova Ul 17 SLO-2000 Maribor Slovenia Univ Veracruzana Dept Inteligencia Artificial Xalapa 91000 Veracruz Mexico

ISBN: (纸本)9781479904549;9781479904532

A new differential evolution algorithm for single objective optimization is presented in this paper. The proposed algorithm uses a self-adaptation mechanism for parameter control, divides its population into more subpopulations, applies more DE strategies, promotes population diversity, and eliminates the individuals that are not changed during some generations. The experimental results obtained by our algorithm on the benchmark consisting of 25 test functions with dimensions D = 10, D = 30, and D = 50 as provided for the CEC 2013 competition and special session on Real Parameter single objective optimization are presented.

关键词： single objective optimization differential evolution self-adaptation

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An Evolutionary Algorithm Using Multi-Strategy Combination for single objective optimization Problem 20

An Evolutionary Algorithm Using Multi-Strategy Combination f...

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20th IEEE International Conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： Chen, Jinhui Chen, Junying Min, Huaqing South China Univ Technol Guangzhou Key Lab Robot & Intelligent Software Sch Software Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781538632215

In this paper, an evolutionary algorithm using multi strategy combination is proposed to solve single objective optimization problem. The algorithm is based on the combination of multi-operator evolutionary algorithms, which uses three evolutionary algorithms, each with multiple search operators, to search global optimum. In order to propose a more efficient search algorithm, interior point method is used to optimize the evolutionary process. The proposed algorithm was tested on the CEC-2014 benchmark, whose experimental results were presented to discuss the features of the proposed algorithm, which showed that the proposed algorithm had efficient iteration and ability to reach good solutions for most problems.

关键词： single objective optimization multi-strategy interior point method evolutionary algorithm

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A Comparative Study of Transfer Functions in Binary Evolutionary Algorithms for single objective optimization 15th

A Comparative Study of Transfer Functions in Binary Evolutio...

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15th International Conference on Distributed Computing and Artificial Intelligence (DCAI)

作者： Sawhney, Ramit Shankar, Ravi Jain, Roopal Netaji Subhas Inst Technol Dept Comp Engn New Delhi India

ISBN: (纸本)9783319946498;9783319946481

Binary versions of evolutionary algorithms have emerged as alternatives to the state of the art methods for optimization in binary search spaces due to their simplicity and inexpensive computational cost. The adaption of such a binary version from an evolutionary algorithm is based on a transfer function that maps a continuous search space to a discrete search space. In an effort to identify the most efficient combination of transfer functions and algorithms, we investigate binary versions of Gravitational Search, Bat Algorithm, and Dragonfly Algorithm along with two families of transfer functions in unimodal and multimodal single objective optimization problems. The results indicate that the incorporation of the v-shaped family of transfer functions in the Binary Bat Algorithm significantly outperforms previous methods in this domain.

关键词： Binary evolutionary algorithm Bat Algorithm Gravitational search Transfer function single objective optimization

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Research on Low-Carbon Building Design Strategies for Folk Dwellings in Hanzhong Based on single objective optimization

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BUILDINGS 2024年第7期14卷 2154页

作者： Yu, Zhichun Guo, Zhenxing Ling, Zihan Chen, Yuren Xi An Jiao Tong Univ Sch Human Settlements & Civil Engn Xian 710049 Peoples R China

With the background of rural revitalization, the urgent demand for energy conservation and improved living quality arises alongside the issues of high energy consumption and low comfort in residential buildings. Located in a region with a hot summer and cold winter climate, Hanzhong faces significant energy consumption for heating and cooling throughout the year, considering both winter insulation and summer heat insulation. Based on the energy consumption simulation and analysis of folk dwellings in Hanzhong, this paper employs a single-objective optimization method to explore the optimization of building envelope structures, including the window-to-wall ratio, bay width, number of floors, orientation, and floor height. Additionally, it investigates building layout, spatial organization, regional design methods, and energy acquisition. Through energy consumption simulation and validation of thermal comfort evaluation index PMV-PPD, design strategies such as building scale, layout organization, indoor and outdoor buffer space design, and building material selection are proposed to effectively improve indoor thermal comfort during the winter and summer seasons. This research provides insights and references for the low-carbon design and optimization of residential buildings.

关键词： single objective optimization low-carbon architecture folk dwellings architectural space digitalization building energy consumption buffer space

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Real Parameter single objective optimization using Self-Adaptive Differential Evolution Algorithm with more Strategies

Real Parameter Single Objective Optimization using Self-Adap...

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IEEE Congress on Evolutionary Computation

作者： Janez Brest Borko Boskovic Ales Zamuda Iztok Fister Efren Mezura-Montes Institute of Computer Science Faculty of Electrical Engineering and Computer Science University of Maribor Departamento de Inteligencia Artificial Universidad Veracruzana

ISBN: (纸本)9781479904532

关键词： single objective optimization Differential evolution Self-adaptation

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Testing MVMO on Learning-based Real-Parameter single objective Benchmark optimization Problems

Testing MVMO on Learning-based Real-Parameter Single Objecti...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Rueda, Jose L. Erlich, Istvan Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands Univ Duisburg Essen Inst Elect Power Syst Duisburg Germany

ISBN: (纸本)9781479974924

Mean-variance mapping optimization (MVMO) is an emerging evolutionary algorithm, which adopts a single-solution based approach and performs evolutionary operations within a normalized range of the search for all optimization variables. MVMO uses a special mapping function for mutation operation, which allows a controlled shift from exploration priority at early stages of the search process to exploitation at later stages. Recently, the MVMO has been extended to a population-based and hybrid variant denoted as MVMO-SH, which includes strategies for local search and multi-parent crossover. This paper provides an study on the performance of MVMO-SH on the IEEE-CEC 2015 competition test suite on learning-based real-parameter single objective optimization. Experimental results evidence the effectiveness of MVMO-SH for successfully solving different optimization problems with different mathematical properties and dimensionality.

关键词： Heuristic optimization mean-variance mapping optimization single objective optimization learning-based optimization

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Modified Adaptive Bats Sonar Algorithm with Doppler Effect Mechanism for Solving single objective Unconstrained optimization Problems 15

Modified Adaptive Bats Sonar Algorithm with Doppler Effect M...

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IEEE 15th International Colloquium on Signal Processing and its Applications (CSPA)

作者： Azlan, N. A. Yahya, N. M. Univ Malaysia Pahang Fac Mfg Engn Cybernat & Syst Engn Lab Pekan Malaysia

ISBN: (纸本)9781538675632

A modified adaptive bats sonar algorithm with Doppler Effect (MABSA-DE) is a new algorithm with an element of Doppler Effect theory that helped the transmitted bats' beam towards a superior position. The performances of the proposed algorithm are validated on a several well-known single objective unconstrained benchmark test functions. The obtained results show that the algorithm can perform well to find an optimum solution. The statistical results of MABSA-DE to solve all the test functions also has been compared with the results from the original MABSA on similar test functions. The comparative study has shown that MABSA-DE outperforms the original algorithm, and thus, it can be an efficient alternative method in solving single objective unconstrained optimization problems.

关键词： Bat Algorithm Doppler Effect Metaheuristic optimization Algorithm single objective optimization

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Runtime Analysis for Maximizing Population Diversity in single-objective optimization 14

Runtime Analysis for Maximizing Population Diversity in Sing...

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Gao, Wanru Neumann, Frank Univ Adelaide Optimisat & Logist Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9781450326629

Recently Ulrich and Thiele [14] have introduced evolutionary algorithms for the mixed multi-objective problem of maximizing fitness as well as diversity in the decision space. Such an approach allows to generate a diverse set of solutions which are all of good quality. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for maximizing the diversity in a population that contains several solutions of high quality. We study how evolutionary algorithms maximize the diversity of a population where each individual has to have fitness beyond a given threshold value. We present a first runtime analysis in this area and study the classical problems called OneMax and LeadingOnes. Our results give first rigorous insights on how evolutionary algorithms can be used to produce a maximal diverse set of solutions in which all solutions have quality above a certain threshold value.

关键词： Runtime analysis single objective optimization Diversity optimization

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