A NOVEL EVOLUTIONARY ALGORITHM ENSEMBLE FOR GLOBAL NUMERICAL OPTIMIZATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350022
Author(s):  
YONGYONG NIU ◽  
ZIXING CAI ◽  
MIN JIN
Keyword(s):  
Evolutionary Algorithm ◽  
Evolutionary Computation ◽  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Covariance Matrix Adaptation ◽  
The Individual ◽  
Benchmark Test Functions ◽  
Adaptation Evolution ◽  
Better Than

In the past few years, evolutionary algorithm ensembles have gradually attracted more and more attention in the community of evolutionary computation. This paper proposes a novel evolutionary algorithm ensemble for global numerical optimization, named NEALE. In order to make a good tradeoff between the exploration and exploitation, NEALE is composed of two constituent algorithms, i.e., the composite differential evolution (CoDE) and the covariance matrix adaptation evolution strategy (CMA-ES). During the evolution, CoDE aims at probing more promising regions and refining the overall quality of the population, while the purposes of CMA-ES are to accelerate the convergence speed and to enhance the accuracy of the solutions. In addition, NEALE encourages the interaction between the constituent algorithms. In NEALE, the interaction is controlled by a predefined generation number and different interaction strategies are designed according to the features of the constituent algorithms. The performance of NEALE has been tested on 25 benchmark test functions developed for the special session on real-parameter optimization of the 2005 IEEE Congress on Evolutionary Computation (IEEE CEC2005). Compared with other state-of-the-art evolutionary algorithms and the individual constituent algorithms, NEALE performs significantly better than them.

scholarly journals Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

2020 ◽  
pp. 1-27
Author(s):  
Anton Bouter ◽  
Tanja Alderliesten ◽  
Peter A.N. Bosman
Keyword(s):  
Evolutionary Algorithm ◽  
Substantial Improvement ◽  
The Real ◽  
Limited Memory ◽  
Covariance Matrix Adaptation ◽  
New Variant ◽  
Estimation Of Distribution ◽  
Adaptation Evolution ◽  
Optimal Mixing ◽  
Better Than

It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, for example, non-separable, multimodal, and multiobjective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMA-ES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting.

scholarly journals An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Lihong Guo ◽  
Gai-Ge Wang ◽  
Heqi Wang ◽  
Dinan Wang
Keyword(s):  
Numerical Optimization ◽  
Firefly Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Convergence Speed ◽  
Function Optimization ◽  
Global Numerical Optimization ◽  
Running Time ◽  
Hybrid Metaheuristic ◽  
Better Than

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

Seasonal streamflow forecast: a GCM multi-model downscaling approach

2010 ◽  
Vol 41 (6) ◽  
pp. 503-507 ◽  
Author(s):  
Kean L. Foster ◽  
Cintia B. Uvo
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Windward Side ◽  
Annual Streamflow ◽  
Principal Source ◽  
Model Ensembles ◽  
The Individual ◽  
Seasonal Streamflow ◽  
Better Than

This work investigates the predictability of seasonal to inter-annual streamflow over several river basins in Norway through the use of multi-model ensembles. As general circulation models (GCMs) do not explicitly simulate streamflow, a statistical link is made between GCM-forecast fields generated in December and average streamflow in the melting season May–June. By using the Climate Predictability Tool (CPT) three models were constructed and from these a multi-model was built. The multi-model forecast is tested against climatology to determine the quality of the forecast. Results from the forecasts show that the multi-model performs better than the individual models and that this method shows improved forecast skills if compared to previous studies conducted in the same basins. The highest forecast skills are found for basins located in the southwest of Norway. The physical interpretation for this is that stations on the windward side of the Scandinavian mountains are exposed to the prevailing winds from the Atlantic Ocean, a principal source of predictive information from the atmosphere on this timescale.

scholarly journals A novel animal migration algorithm for global numerical optimization

2016 ◽  
Vol 13 (1) ◽  
pp. 259-285 ◽  
Author(s):  
Qifang Luo ◽  
Mingzhi Ma ◽  
Yongquan Zhou
Keyword(s):  
Numerical Optimization ◽  
Cuckoo Search ◽  
Test Functions ◽  
Migration Process ◽  
Global Numerical Optimization ◽  
Bee Colony ◽  
Intelligent Optimization Algorithms ◽  
Animal Migration Optimization ◽  
Animal Migration ◽  
Benchmark Test Functions

Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).

Evolutionary Lagrangian Inverse Modeling for PM10 Pollutant Dispersion

2010 ◽  
pp. 293-312 ◽  
Author(s):  
María Victoria Toro
Keyword(s):  
Particulate Matter ◽  
Evolutionary Computation ◽  
Inverse Modeling ◽  
Evolutionary Model ◽  
Pollutant Dispersion ◽  
Time Behavior ◽  
Proposed Model ◽  
The Individual ◽  
Over Time

One of the main concerns when it comes to mitigating the effects of the concentration of the particulate matter PMx in an area of study is the fact to determine its behavior over time, overcoming both physical and mathematical limitations in terms of a phenomenon of dispersion. Therefore, this chapter develops and analyzes a model based on the principles of evolutionary computation (EC) in order to determine the space-time behavior of the concentration of the particulate matter PMx in a study area. The proposed model has three submodels within an integrated solution, which constitute the individual to evolve. The transformation of the possible solutions or generational population is made by using an asynchronous evolutionary model, due to genetic dependency between substructures. The proposed model was validated for configurations of n sources of emissions and m monitoring stations that measure the quality of the air in a study area.

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