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The Challenge for the Nature-Inspired Global Optimization Algorithms: Non-Symmetric benchmark functions

IEEE ACCESS 2021年 9卷 106317-106339页

作者： Gao, Zheng-Ming Zhao, Juan Hu, Yu-Rong Chen, Hua-Feng Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Dept Sci & Technol Jingmen 448000 Peoples R China

Along with the increasing number of nature-inspired algorithms, more and more benchmark functions were also involved in the initial verification experiments. The benchmark functions were introduced to verify the capability of algorithms in optimization, but not all of them could be optimized, because they were different from each other in dimensionality, separability, scalability, and modality ***.. In this paper, we introduced another property called symmetry or non-symmetry, which should be another embedded characteristic of functions affecting the capability of algorithms in optimization. 67 non-symmetric benchmark functions were collected and 9 popular capability-verified algorithms were introduced in four types of simulation experiments. Experimental results show that most of the non-symmetric algorithms could not be optimized. And none of the algorithms involved could optimize them all. Efforts remain in need of new methods and improvements of nature-inspired algorithms.

关键词： benchmark testing Optimization Classification algorithms Genetic algorithms Scalability Rabbits Mathematical model Nature-inspired algorithm optimization algorithms benchmark functions non-symmetry

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Validation of Well-Known Population-Based Stochastic Optimization Algorithms Using benchmark functions 7th

Validation of Well-Known Population-Based Stochastic Optimiz...

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International Conference on Soft Computing for Problem Solving (SocProS)

作者： Nayak, Byamakesh Dash, Srikanta Kumar Sahu, Jiban Ballav KIIT Univ Sch Elect Engn Bhubaneswar India

ISBN: (纸本)9789811315923;9789811315916

Test functions are used for validation of reliability and the implementation of optimization process. There are a lot of benchmarked test functions reported in the literature. The classifications of benchmarked functions are based on steady, differentiable, unable to be separated and scaled or having one or several modes or maxima and dimensions. In this paper, the test functions are properly selected as objective functions with various properties in terms of modality, dimension, valleys, etc., for validation of well-known population-based stochastic optimization algorithms. The novelty of this paper is to strengthen the validation of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) algorithms using ten well-known benchmark functions.

关键词： Validation Grey wolf optimization (GWO) Particle swarm optimization (PSO) benchmark functions

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Performance of Different Techniques Applied in Genetic Algorithm towards benchmark functions

Performance of Different Techniques Applied in Genetic Algor...

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5th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Lim, Seng Poh Haron, Habibollah Univ Teknol Malaysia Fac Comp Dept Comp Sci Skudai 81310 Johor Malaysia

ISBN: (纸本)9783642365461

Optimisation is the most interesting problems to be tested by using Artificial Intelligence (AI) methods because different optimal results will be obtained when different methods are implemented. Yet, there is no exact solution from the methods implemented because random function is usually applied. Genetic algorithm is a popular method which is used to solve the optimisation problems. However, no any methods can execute perfectly because the way of the method performs is different. Therefore, this paper proposed to compare the performance of GA with different operation techniques by using the benchmark functions. This can prove that different techniques applied in the operations can let GA produces different result. Based on the experiment result, GA is proved to perform well in the optimisation problems but it highly depends on the techniques implemented. The techniques for each operation have shown different performance in obtaining the time, minimum and average values for benchmark functions.

关键词： Genetic Algorithm Optimisation benchmark functions Performance

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Performance Comparison of Genetic Algorithm, Differential Evolution and Particle Swarm Optimization Towards benchmark functions

Performance Comparison of Genetic Algorithm, Differential Ev...

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IEEE Conference on Open Systems (ICOS)

作者： Lim, Seng Poh Haron, Habibollah Univ Teknol Malaysia Fac Comp Dept Comp Sci Skudai 81310 Johor Malaysia

ISBN: (纸本)9781479902866

Genetic algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) are always implemented to solve different kinds of complex optimization problems. Each method contains its own advantages and the performance varies based on different case studies. There are many Soft Computing (SC) methods which can generate different result for the same optimization problems. However, no exact result is produced because random function is usually applied in SC methods. The performance maybe is affected by the parameter setting or operations inside each method. Therefore, the motivation of this paper is to compare the performance of GA, DE and PSO by using the same parameters setting and optimization problems. The experiments can prove that although same parameters setting are applied, but different fitness and time can be obtained. Based on the result, GA was proven to perform better compared to DE and PSO in obtaining highest number of best minimum fitness and faster than both methods.

关键词： Genetic Algorithm Differential Evolution Particle Swarm Optimization Optimization benchmark functions Performance

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An Improved Particle Swarm Optimization Algorithm applied to benchmark functions 8

An Improved Particle Swarm Optimization Algorithm applied to...

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8th IEEE International Conference on Intelligent Systems (IS)

作者： Uriarte, Alfonso Melin, Patricia Valdez, Fevrier Tijuana Inst Technol Div Grad Studies & Res Tijuana Mexico

ISBN: (纸本)9781509013531

According to the literature of Particle Swarm Optimization (PSO), there are problems of getting stuck at local minima and premature convergence with this algorithm. A new algorithm is presented in this paper called the Improved Particle Swarm Optimization using the gradient descent method as an operator incorporated into the Algorithm, as a function to achieve the improvement. The gradient descent method (BP Algorithm) helps not only to increase the global optimization ability, but also to avoid the premature convergence problem. The Improved PSO Algorithm IPSO is applied to benchmark functions. The results show that there is an improvement with respect to using the conventional PSO Algorithm.

关键词： Particle Swarm Optimization Gradient Descent Method benchmark functions

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Comparison of Heuristic Algorithms with Performance Metrics on Multimodal benchmark functions 2

Comparison of Heuristic Algorithms with Performance Metrics ...

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2017 International Conference on Computer Science and Engineering (UBMK)

作者： Bastug, Ayse Karakuzu, Cihan Bilecik Seyh Edebali Univ Yonetim Bilisim Sistemleri Bolumu Iktisadi & Idari Bilimler Fak Bilecik Turkey Bilecik Seyh Edebali Univ Bilgisayar Muhendisligi Bolumu Muhendisl Fak Bilecik Turkey

ISBN: (纸本)9781538609309

The solution of difficult problems can be realized in shorter time with heuristic algorithms. There are many heuristic algorithms. In this study, artificial bee colony (ABC), biogeography based optimization (BBO), cuckoo bird search algorithm (CSO), differential evolution (DE), imperialist competitive algorithm (ICA) and particle swarm algorithm (PSO) have been chosen due to reasons such as the widespread use in the literature and the large number of open source code applications to use it widely in the literature and to have a lot of open source code applications. Each of these preferred algorithms has been run 30 times in a 10-dimensional search space with the same initial positions and conditions to find the global minimum point on the 5 benchmark function, which is also frequently used in the scientific world. The performance of the algorithms based on the results obtained from the runs has been determined by the best cost, worst cost, accuracy, stability, time and standard deviation performance metrics. The performance scores of the algorithms are evaluated based on the cumulative average ranking value generated from the results of these performance metrics.

关键词： heuristic algorithms swarm intelligence performance metrics benchmark functions

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Comparative Analysis of Swarm-Based Metaheuristic Algorithms on benchmark functions 8th

Comparative Analysis of Swarm-Based Metaheuristic Algorithms...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Hussain, Kashif Salleh, Mohd Najib Mohd Cheng, Shi Shi, Yuhui Univ Tun Hussein Onn Malaysia Batu Pahat Malaysia Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9783319618241;9783319618234

Swarm-based metaheuristic algorithms inspired from swarm systems in nature have produced remarkable results while solving complex optimization problems. This is due to their capability of decentralized control of search agents able to explore search environment more effectively. The large number of metaheuristics sometimes puzzle beginners and practitioners where to start with. This experimental study covers 10 swarm-based metaheuristic algorithms introduced in last decade to be investigated on their performances on 12 test functions of high dimensions with diverse features of modality, scalability, and valley landscape. Based on simulations, it can be concluded that firefly algorithm outperformed rest of the algorithms while tested unimodal functions. On multimodal functions, animal migration algorithm produced outstanding results as compared to rest of the methods. In future, further investigation can be conducted on relating benchmark functions to real-world optimization problem so that metaheuristic algorithms can be grouped according to suitability of problem characteristics.

关键词： Swarm-intelligence Swarm-based algorithms Metaheuristic benchmark functions

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Investigation of Self-adapting Genetic Algorithms Using Some Multimodal benchmark functions

Investigation of Self-adapting Genetic Algorithms Using Some...

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3rd International Conference on Computational Collective Intelligence (ICCCI 2011)

作者： Smetek, Magdalena Trawinski, Bogdan Wroclaw Univ Technol Inst Informat PL-50370 Wroclaw Poland

ISBN: (纸本)9783642239342;9783642239359

Self-adaptive mutation, crossover, and selection were implemented and applied in three genetic algorithms. So developed self-adapting algorithms were then compared, with respect to convergence, with a standard genetic one, which contained constant rates of mutation and crossover. The experiments were conducted using five multimodal benchmark functions. The analysis of the results obtained was supported by nonparametric Friedman and Wilcoxon signed-rank tests. The algorithm employing self-adaptive selection revealed the best performance.

关键词： self-adapting GA self-adaptive selection benchmark functions

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Comparative Performance Evaluation of Teaching Learning Based Optimization against Genetic Algorithm on benchmark functions

Comparative Performance Evaluation of Teaching Learning Base...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Ebraheem, M. Jyothsna, T. R. GITAM Univ GITAM Inst Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India AU Coll Engn Dept Elect Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781479974559

In this paper effectiveness of newly introduced 'teaching learning based optimization' (TLBO) is evaluated against different benchmark optimization problems. The effectiveness, then, is compared with the performance of genetic algorithm (GA) using the same parameters as used with TLBO. The functions on which the two algorithms applied in this work are rastrigin function, quartic, rosenbrock, six hump camel back. And the results are validated. Both the algorithms are applied on these functions using the same software (MATLAB) on the same platform also with the same number of population and elite group. The effectiveness of the TLBO is compared with that of GA based on the speed, average solution.

关键词： Teaching Learning based optimization Genetic Algorithm benchmark functions

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Local Optima Networks of the Black Box Optimisation benchmark functions

Local Optima Networks of the Black Box Optimisation Benchmar...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Mitchell, Paul Ochoa, Gabriela Chassagne, Romain Heriot Watt Univ Edinburgh Midlothian Scotland Univ Stirling Stirling Scotland Bur Rech Geol & Minieres Orleans France

ISBN: (纸本)9798400701207

Compressed monotonic local optima networks (CMLONs) provide a powerful means to visualise the global structure of the fitness landscapes of optimisation problems as network graphs. Historically, they have been developed for discrete optimisation problems, but they have more recently been extended to continuous problems. However, experience is limited because they have only been applied to a few analytical problems and even fewer real-world problems. This work aims to address that gap by providing a systematic and comprehensive catalogue of CMLONs for the well-known black box optimisation benchmark functions. These are a set of continuous analytical functions with diverse properties. CMLONs are calculated for each of the twenty-four benchmark functions in three, five, eight, twelve, and twenty dimensions. Network metrics are also calculated for each of the CMLONs and dimensionality reduction used to classify and compare the functions. It was found that the CMLONs have diverse representations that are related to both the functions' properties and their dimensionality. Network metrics were important for multimodal functions in higher dimensional problems, where the CMLONs were too dense to visualise meaningfully. These results provide an extensive catalogue of CMLONs for a variety of continuous functions and provide a useful reference for real-world optimisation problems.

关键词： Local optima networks fitness landscape benchmark functions

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