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Birds foraging search: a novel population-based algorithm for global optimization

MEMETIC COMPUTING 2019年第3期11卷 221-250页

作者： Zhang, Zhuoran Huang, Changqiang Dong, Kangsheng Huang, Hanqiao Air Force Engn Univ Aeronaut & Astronaut Engn Coll Xian 710038 Shaanxi Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Shaanxi Peoples R China

population-based algorithms have become a research hotspot for optimization problems and have been widely applied in various fields in recent decades. This paper presents the birds foraging search (BFS) algorithm, which is a novel population-based optimizer inspired by the different behaviors of birds during the foraging process for solving global optimization problems. The overall framework of the proposed algorithm involves three phases: the flying search behavior phase, the territorial behavior phase and the cognitive behavior phase. In the proposed algorithm, the first two phases balance the exploration and exploitation capabilities of the algorithm, and the third phase enhances the search efficiency. Classical benchmarks and CEC2014 benchmarks are employed to fully evaluate the performance of our BFS. The statistical results reveal that the BFS algorithm outperforms other conventional approaches and state-of-the art algorithms in terms of accuracy and convergence.

关键词： population-based algorithm Global optimization Birds foraging search Unconstrained optimization problems

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A Selective population-based algorithm with Multi-Perturbative Operators for Traveling Salesman Problems 6

A Selective Population-based Algorithm with Multi-Perturbati...

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6th International Conference on Robotics and Automation Sciences (ICRAS)

作者： Chen, Yuning Ouyang, Chun Hu, Linqiang Zhou, Ziqing Guan, Yuxiang Gan, Zhongxue Fudan Univ Acad Engn & Technol Shanghai Peoples R China Engn Res Ctr Intelligent Robot ERCIR Jihua Lab Foshan Peoples R China

ISBN: (数字)9781665467841

ISBN: (纸本)9781665467841

A selective population-based algorithm with multi-perturbative operators is proposed in this article for the classical combinatorial optimization problem: traveling salesman problem. The proposed algorithm is composed of two important components: perturbative operator set and operator management method. Two types of operators are contained in the perturbative operator set: (1) five basic operators that are most commonly used by metaheuristics in solving traveling salesman problem, (2) five composite operators that are proposed based on the execution sequence of five basic operators. A scoring table method is proposed in this article to manage the operators in the set. Thirty-seven datasets from TSPLIB are adopted to demonstrate the performance of the proposed algorithm. From the experimental results, two phenomena are observed: (1) composite operators obtain better solutions than basic operators on all datasets while consume more running time than basic operators especially on larger datasets, (2) scoring table mechanism enables the algorithm to obtain better solutions on 34 of 37 datasets. Additionally, among eight state-of-the-art metaheuristics, the proposed algorithm outperforms six of them on all datasets used while outperforms the remaining two on 9 of 11 datasets and 21 of 23 datasets respectively.

关键词： perturbative operator scoring table method population-based algorithm traveling salesman problem

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Discrete Differentiated Creative Search for traveling salesman problem

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Xu, Qi Xia, Kewen Chu, Xiaoyu Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

A novel population-based Discrete Differentiated Creative Search (DDCS) is proposed in this paper for solving the traveling salesman problem (TSP). DDCS introduces greedy beam search to adaptively initialize the population and improve the quality of the initial solutions. Second, a multi-edge construction operator, edge-based mathematical operations and a similarity attraction operator are used to guide individuals from different population categories towards higher-quality solutions based on the current solutions. Finally, a random nearest neighbor replacement strategy is used to replace individuals with the same distance heuristically, reducing the assimilation rate of the population. DDCS is tested with 50 instances from TSPLIB and compared with a variety of stateof-the-art and variants of classical algorithms. The results demonstrate that DDCS exhibits superior optimization capability and higher stability.

关键词： population-based algorithm Traveling salesman problem Edge-based operations Random nearest neighbor replacement Greedy beam search

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On the convergence of a population-based global optimization algorithm

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JOURNAL OF GLOBAL OPTIMIZATION 2004年第2-3期30卷 301-318页

作者： Birbil, SI Fang, SC Sheu, RL Erasmus Univ Erasmus Res Inst Management NL-3000 DR Rotterdam Netherlands N Carolina State Univ Ind Engn & Operat Res Raleigh NC 27695 USA Natl Cheng Kung Univ Dept Math Tainan 701 Taiwan

In global optimization, atypical population-based stochastic search method works on a set of sample points from the feasible region. In this paper, we study a recently proposed method of this sort. The method utilizes an attraction-repulsion mechanism to move sample points toward optimality and is thus referred to as electromagnetism-like method (EM). The computational results showed that EM is robust in practice, so we further investigate the theoretical structure. After reviewing the original method. we present some necessary modifications for the convergence proof. We show that in the limit. the modified method converges to the vicinity of global optimum with probability one.

关键词： stochastic search method population-based algorithm convergence with probability one

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On the convergence of a population-based global optimization algorithm

On the convergence of a population-based global optimization...

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International Conference on Optimization and Optimal Control (ICOOC)

关键词： stochastic search method population-based algorithm convergence with probability one

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Structural bias in population-based algorithms

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INFORMATION SCIENCES 2015年 298卷 468-490页

作者： Kononova, Anna V. Corne, David W. De Wilde, Philippe Shneer, Vsevolod Caraffini, Fabio Heriot Watt Univ Dept Comp Sci Edinburgh EH14 4AS Midlothian Scotland Univ Kent Sch Comp Canterbury CT2 7NZ Kent England Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Midlothian Scotland De Montfort Univ Sch Comp Sci & Informat Ctr Computat Intelligence Leicester LE1 9BH Leics England Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40014 Finland

Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of 'black box' optimisation algorithms. The latter are designed to be able to address any optimisation problem, requiring only that the quality of any candidate solution can be calculated via a 'fitness function' specific to the problem. For such algorithms to be successful, at least three properties are required: (i) an effective informed sampling strategy, that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates;(ii) mechanisms to ensure efficiency, so that (for example) the same candidates are not repeatedly visited;and (iii) the absence of structural bias, which, if present, would predispose the algorithm towards limiting its search to specific regions of the solution space. The first two of these properties have been extensively investigated, however the third is little understood and rarely explored. In this article we provide theoretical and empirical analyses that contribute to the understanding of structural bias. In particular, we state and prove a theorem concerning the dynamics of population variance in the case of real-valued search spaces and a 'flat' fitness landscape. This reveals how structural bias can arise and manifest as non-uniform clustering of the population over time. Critically, theory predicts that structural bias is exacerbated with (independently) increasing population size, and increasing problem difficulty. These predictions, supported by our empirical analyses, reveal two previously unrecognised aspects of structural bias that would seem vital for algorithm designers and practitioners. Respectively, (i) increasing the population size, though ostensibly promoting diversity, will magnify any inherent structural bias, and (ii) the effects of structural bias are more apparent when faced with (many cla

关键词： Structural bias algorithmic design Evolutionary computation population-based algorithm Optimisation

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Adaptive recommendation model using meta-learning for population-based algorithms

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INFORMATION SCIENCES 2019年 476卷 192-210页

作者： Chu, Xianghua Cai, Fulin Cui, Can Hu, Mengqi Li, Li Qin, Quande Shenzhen Univ Coll Management Shenzhen Peoples R China Shenzhen Univ Inst Big Data Intelligent Management & Decis Shenzhen Peoples R China Arizona State Univ Sch Comp Informat Decis Syst Engn Tempe AZ 85287 USA Univ Illinois Dept Mech & Ind Engn Chicago IL USA

To efficiently solve complex optimization problems, numerous population-based meta heuristics and extensions have been developed. However, the performances of the algorithms vary depending on the problems. In this research, we propose an Adaptive Recommendation Model (ARM) using meta-learning to identify appropriate problem-dependent population-based algorithm. In ARM, the algorithms are adaptively selected by mapping the problem characteristics to the algorithm performance. Since the meta-features extracted and meta-learner adopted would significantly affect the system performance, 18 meta-features including statistical, geometrical and landscape features are extracted to characterize optimization problem spaces. Both instance-based and model-based learners are investigated. Two performance metrics, Spearman's rank correlation coefficient and success rate are used to evaluate the accuracy of optimizer's ranking prediction and the precision of the best optimizer recommendation. The proposed ARM is compared against population-based algorithms with distinct search capabilities such as PSO variants, non-PSO population-based optimizers, hyper-heuristics and ensemble methods. Benchmark functions and real-world problems with various properties are adopted in the experiments. Experimental results reveal the extendibility and effectiveness of ARM on the diverse tested problems in terms of solution accuracy, ranking and success rate. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Global optimization algorithm selection Meta-learning population-based algorithm Recommendation system

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Optimal Control Design for Maneuverable Aircraft Using population-based algorithms 13

Optimal Control Design for Maneuverable Aircraft Using Popul...

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13th International Symposium on Intelligent Systems (INTELS)

作者： Korsun, O. N. Sergeev, S. A. Stulovskii, A. V. Fed State Unitary Enterprise State Res Inst Aviat Moscow 125319 Russia Moscow Automobile & Rd Construct State Tech Univ Moscow 125319 Russia

The report outlines the algorithmic and methodological approaches to solving the problem of the optimal program control design for the maneuverable aircraft. An object model convenient for generating program control is shown. This paper demonstrates possibility of solving considered problem using direct method, involving cubic Hermite splines and a population-based optimization algorithm for generating control signals. This report also compares proposed direct method with classic approaches to optimal control. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： optimal control direct optimization population-based algorithm aircraft

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algorithm based on population With a Flexible Search Mechanism

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IEEE ACCESS 2019年 7卷 132253-132270页

作者： Lapa, Krystian Cpalka, Krzysztof Zalasinski, Marcin Czestochowa Tech Univ Inst Computat Intelligence PL-42200 Czestochowa Poland

In this paper, we propose a new population-based algorithm (PBA) that adjusts its search mechanism to the problem which is to be optimized. It uses a set of search operators used in commonly-used PBAs and this selection can be made freely. The proposed algorithm selects two operators from this set, i.e. one for exploration and the other for exploitation of the search space of the solution. This approach can be an alternative for finding new inspirations for the development of PBAs and for modifying the existing PBAs. The effectiveness of the proposed algorithm has been tested using typical benchmarks for testing PBAs. An interesting aspect of the performed simulations is the analysis of how operators are used during the optimization process.

关键词： Evolutionary computation particle swarm optimization genetic algorithms population-based algorithm selection of the search mechanism selection of operators flexible search mechanism

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Optimal Control Design for Maneuverable Aircraft Using population-based algorithms

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Procedia Computer Science 2019年 150卷 361-367页

作者： O.N. Korsun S.A. Sergeev A.V. Stulovskii Federal State Unitary Enterprise State Research Institute of Aviation Systems Moscow 125319 Russia Moscow Automobile and Road Construction State Technical University (MADI) Moscow 125319 Russia

关键词： optimal control direct optimization population-based algorithm aircraft

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