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Experimental Comparison of Six population-based algorithms for Continuous Black Box Optimization

EVOLUTIONARY COMPUTATION 2012年第4期20卷 483-508页

作者： Posik, Petr Kubalik, Jiri Czech Tech Univ Fac Elect Engn CR-16635 Prague Czech Republic

Six population-based methods for real-valued black box optimization are thoroughly compared in this article. One of them, Nelder-Mead simplex search, is rather old, but still a popular technique of direct search. The remaining five (POEMS, G3PCX, Cauchy EDA, BIPOP-CMA-ES, and CMA-ES) are more recent and came from the evolutionary computation community. The recently proposed "comparing continuous optimizers" (COCO) methodology was adopted as the basis for the comparison. The results show that BIPOP-CMA-ES reaches the highest success rates and is often also quite fast. The results of the remaining algorithms are mixed, but Cauchy EDA and POEMS are usually slow.

关键词： Benchmarking real-valued black box optimization population-based algorithms estimation of distribution algorithm evolutionary strategy covariance matrix adaptation

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Optimizing the Heliostat Field Layout by Applying Stochastic population-based algorithms

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INFORMATICA 2018年第1期29卷 21-39页

作者： Cruz, Nicolas C. Redondo, Juana L. Alvarez, Jose D. Berenguel, Manuel Ortigosa, Pilar M. Univ Almeria ceiA3 Informat Dept Cordoba Spain

The heliostat field of Solar Central Receiver Systems takes up to 50% of the initial investment and can cause up to 40% of energetic loss in operation. Hence, it must be carefully optimized Design procedures usually rely on particular heliostat distribution models. In this work, optimization of the promising biomimetic distribution model is studied. Two stochastic population-based optimizers are applied to maximize the optical efficiency of fields: a genetic algorithm, micraGA, and a memetic one, UEGO. As far as the authors know, they have not been previously applied to this problem. However, they could be a good option according to their structure. Additionally, a Brute-Force Grid is used to estimate the global optimum and a Pure-Random Search is applied as a baseline reference. Our empirical results show that many different configurations of the distribution model lead to very similar solutions. Although micraGA exhibits poor performance, UEGO achieves the best results in a reduced time and seems appropriate for the problem at hand.

关键词： heliostat field optimization population-based algorithms

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Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics

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INFORMATION SCIENCES 2019年 489卷 193-204页

作者： Lapa, Krystian Czestochowa Tech Univ Ul Dabrowskiego 69 PL-42201 Czestochowa Poland

In this paper, a method for optimizing the parameters of multi-objective population-based algorithms is proposed. It is based on a meta-optimization, in which an external algorithm optimizes the parameters of the main algorithm. The purpose of proposed optimization is to select the functions, on the basis of which the values of parameters of the main algorithm are calculated for each algorithm iteration. The assumption was made that the functions should be selected in order to achieve the best possible Pareto front in terms of multi-objective performance metrics. The proposed method has been designed to allow the optimization of any algorithms and thus to avoid the time consuming manual selection of their parameters. To compare the presented method with other approaches, a set of well-known modeling benchmarks were used. (C) 2019 Elsevier Inc. All rights reserved.

关键词： population-based algorithms Multi-objective Performance metrics Meta-optimization

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Simple population-based algorithms for solving optimization problems

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RAIRO-OPERATIONS RESEARCH 2024年第1期58卷 253-280页

作者： Baskar, Amaladosan Panimalar Inst Technol Chennai 600123 India

Heuristic algorithms are simple yet powerful tools that are capable of yielding acceptable results in a reasonable execution time. Hence, they are being extensively used for solving optimization problems by researchers nowadays. Due to the quantum of computing power and hardware available today, a large number of dimensions and objectives are considered and analyzed effectively. This paper proposes new population-based metaheuristic algorithms that are capable of combining different strategies. The new strategies help in fast converging as well as trying to avoid local optima. The proposed algorithms could be used as single-phase as well as two-phase algorithms with different combinations and tuning parameters. "Best", "Mean" and "Standard Deviation" are computed for thirty trials in each case. The results are compared with many efficient optimization algorithms available in the literature. Sixty-one popular un-constrained benchmark problems with dimensions varying from two to thousand and fifteen constrained real-world engineering problems are used for the analyses. The results show that the new algorithms perform better for several test cases. The suitability of the new algorithms for solving multi-objective optimization problems is also studied using five numbers of two-objective ZDT problems. Pure Diversity, Spacing, Spread and Hypervolume are the metrics used for the evaluation.

关键词： Optimization population-based algorithms performance metrics benchmark functions

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Comparison of population-based algorithms for optimizing thinnings and rotation using a process-based growth model

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SCANDINAVIAN JOURNAL OF FOREST RESEARCH 2019年第6期34卷 458-468页

作者： Xue, Hailian Makela, Annikki Valsta, Lauri Vanclay, Jerome K. Cao, Tianjian Northwest A&F Univ Yangling Simulat Optimizat Lab Yangling Shaanxi Peoples R China Univ Helsinki Dept Forest Sci Helsinki Finland Southern Cross Univ Forest Res Ctr Lismore NSW Australia

Stand management optimization has long been computationally demanding as increasingly detailed growth and yield models have been developed. Process-based growth models are useful tools for predicting forest dynamics. However, the difficulty of classic optimization algorithms limited its applications in forest planning. This study assessed alternative approaches to optimizing thinning regimes and rotation length using a process-based growth model. We considered (1) population-based algorithms proposed for stand management optimization, including differential evolution (DE), particle swarm optimization (PSO), evolution strategy (ES), and (2) derivative-free search algorithms, including the Nelder-Mead method (NM) and Osyczka's direct and random search algorithm (DRS). We incorporated population-based algorithms into the simulation-optimization system OptiFor in which the process-based model PipeQual was the simulator. The results showed that DE was the most reliable algorithm among those tested. Meanwhile, DRS was also an effective algorithm for sparse stands with fewer decision variables. PSO resulted in some higher objective function values, however, the computational time of PSO was the longest. In general, of the population-based algorithms, DE is superior to the competing ones. The effectiveness of DE for stand management optimization is promising and manifested.

关键词： Algorithm performance optimal thinning population-based algorithms process-based model

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Dynamic Signature Vertical Partitioning Using Selected population-based algorithms 20th

Dynamic Signature Vertical Partitioning Using Selected Popul...

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20th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Zalasinski, Marcin Niksa-Rynkiewicz, Tacjana Cpalka, Krzysztof Czestochowa Tech Univ Dept Computat Intelligence Czestochowa Poland Gdansk Univ Technol Dept Marine Mechatron Gdansk Poland

ISBN: (纸本)9783030878979;9783030878962

The dynamic signature is a biometric attribute used for identity verification. It contains information on dynamics of the signing process. There are many approaches to the dynamic signature verification, including the one based on signature partitioning. Partitions are the regions created on the basis of signals describing the dynamics of the signature. They contain information on the shape of the signature characteristic of a given individual. In this paper, we focus on so-called vertical partitioning and different population-based algorithms which are used to determine partition division points. In the verification process we use an authorial one-class classifier.

关键词： Dynamic signature verification population-based algorithms Fuzzy systems Biometrics

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Efficiency Criteria as a Solution to the Uncertainty in the Choice of population Size in population-based algorithms Applied to Water Network Optimization

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WATER 2016年第12期8卷 583页

作者： Mora-Melia, Daniel Gutierrez-Bahamondes, Jimmy H. Iglesias-Rey, Pedro L. Javier Martinez-Solano, F. Univ Talca Fac Ingn Dept Ingn & Gest Construcc Talca 3340000 Chile Univ Politecn Valencia Dept Ingn Hidraul & Medio Ambiente E-46022 Valencia Spain

Different population-based algorithms (PbAs) have been used in recent years to solve all types of optimization problems related to water resource issues. However, the performances of these techniques depend heavily on correctly setting some specific parameters that guide the search for solutions. The initial random population size P is the only parameter common to all PbAs, but this parameter has received little attention from researchers. This paper explores P behaviour in a pipe-sizing problem considering both quality and speed criteria. To relate both concepts, this study applies a method based on an efficiency ratio E. First, specific parameters in each algorithm are calibrated with a fixed P. Second, specific parameters remain fixed, and the initial population size P is modified. After more than 600,000 simulations, the influence of P on obtaining successful solutions is statistically analysed. The proposed methodology is applied to four well-known benchmark networks and four different algorithms. The main conclusion of this study is that using a small population size is more efficient above a certain minimum size. Moreover, the results ensure optimal parameter calibration in each algorithm, and they can be used to select the most appropriate algorithm depending on the complexity of the problem and the goal of optimization.

关键词： population-based algorithms pipe-sizing problem water distribution networks optimization population size

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AModel for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of population- based algorithms

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EVOLUTIONARY COMPUTATION 2014年第1期22卷 159-188页

作者： Turkey, Mikdam Poli, Riccardo Univ Essex Sch Comp Sci Colchester CO4 3SQ Essex England

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

关键词： Collective behaviour analysis exploitation population dynamics population-based algorithms evolutionary algorithms emergent features self-organising maps

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Majority voting for discrete population-based optimization algorithms

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SOFT COMPUTING 2019年第1期23卷 1-18页

作者： Mahdavi, Sedigheh Rahnamayan, Shahryar Mahdavi, Abbas Univ Ontario Inst Technol Dept Elect Comp & Software Engn Oshawa ON Canada Vali E Asr Univ Rafsanjan Fac Math Sci Dept Stat Rafsanjan Iran

population-based metaheuristic algorithms have been extensively applied to solve discrete optimization problems. Generally speaking, they work with a set of candidate solutions in the population which evolve during generations using variant reproduction and selection operations to find the optimal solution(s). The population is similar to a small society having several individuals which seek a common goal/solution. This study is motivated from the election systems of societies which can be applied in the population-based algorithms. We propose utilizing the majority voting for discrete population-based optimization algorithms which uses the information of all candidate solutions in the current generation to create a new trial candidate solution, called a president candidate solution. During optimization process, after applying the evolutionary operations, all candidate solutions vote collectively to determine the values of the president's variables. In the proposed method, a majority voting is utilized to choose a value for each variable (gene) of the president candidate solution. This method keeps untouched all other steps of population-based algorithms;therefore, it can be used with any kind of population-based algorithm. As case studies, the discrete differential evolution (DDE) algorithm and the discrete particle swarm optimization (DPSO) are used as the parent algorithms to develop majority voting-based discrete DE (MVDDE) and majority voting-based discrete PSO (MVDPSO). These two algorithms are evaluated on the fifteen discrete benchmark functions with the dimensions of D = 10, 30, 50, 100, 200 and 500. Simulation results confirm that majority voting-based discrete optimization algorithms obtain a promising performance on the majority of the benchmark functions. In addition, we have conducted some tests on large-scale 0-1 knapsack problems with large scales as a real-world application.

关键词： Election Majority voting Evolutionary algorithms Differential evolution (DE) population-based algorithms President Particle swarm optimization (PSO) Metaheuristics Discrete optimization Collective intelligence

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A hybridization approach with predicted solution candidates for improving population-based optimization algorithms

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INFORMATION SCIENCES 2021年 574卷 133-161页

作者： Oszust, Mariusz Sroka, Grzegorz Cymerys, Karol Rzeszow Univ Technol Dept Comp & Control Engn Wincentego Pola 2 PL-35959 Rzeszow Poland Rzeszow Univ Technol Dept Topol & Algebra Powstancow Warszawy 12 PL-35959 Rzeszow Poland

The recent proliferation of population-based meta-heuristics designed for solving optimization problems and their successes confirm that more promising techniques inspired by physical phenomena or biological systems are desired. Therefore, in this paper, a novel hybridization approach is proposed to improve the performance of optimization algorithms. The approach replaces a small number of worst solutions obtained by a meta-heuristic with predicted candidates without altering its search operators. Specifically, a target fitness value of the predicted candidate is determined based on the fitness of the population and a search strategy. Then, a calibration problem is solved to infer its decision variables. In this study, the proposed hybridization technique is applied to ten state-of-the-art population-based algorithms. The meta-heuristics and hybrids are evaluated on 82 functions, four engineering problems, and a new challenging problem of estimating a constant in Markov's inequality using minimal polynomials of different degrees. The experimental results reveal the superiority of the hybrids over their counterparts and confirm the suitability of the proposed approach for improving the efficiency of meta-heuristics. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Global optimization population-based algorithms Meta-heuristics Hybridization Inverse regression Calibration problem

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