scholarly journals Big Archive-Assisted Ensemble of Many-Objective Evolutionary Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wen Zhong ◽  
Jian Xiong ◽  
Anping Lin ◽  
Lining Xing ◽  
Feilong Chen ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Large Scale ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Evolutionary Process ◽  
Superior Performance ◽  
Nondominated Solutions ◽  
Environmental Selection ◽  
Computational Resources ◽  
Better Than

Multiobjective evolutionary algorithms (MOEAs) have witnessed prosperity in solving many-objective optimization problems (MaOPs) over the past three decades. Unfortunately, no one single MOEA equipped with given parameter settings, mating-variation operator, and environmental selection mechanism is suitable for obtaining a set of solutions with excellent convergence and diversity for various types of MaOPs. The reality is that different MOEAs show great differences in handling certain types of MaOPs. Aiming at these characteristics, this paper proposes a flexible ensemble framework, namely, ASES, which is highly scalable for embedding any number of MOEAs to promote their advantages. To alleviate the undesirable phenomenon that some promising solutions are discarded during the evolution process, a big archive that number of contained solutions be far larger than population size is integrated into this ensemble framework to record large-scale nondominated solutions, and also an efficient maintenance strategy is developed to update the archive. Furthermore, the knowledge coming from updating archive is exploited to guide the evolutionary process for different MOEAs, allocating limited computational resources for efficient algorithms. A large number of numerical experimental studies demonstrated superior performance of the proposed ASES. Among 52 test instances, the ASES performs better than all the six baseline algorithms on at least half of the test instances with respect to both metrics hypervolume and inverted generational distance.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

Large-scale economic load dispatch using squirrel search algorithm

2020 ◽  
Vol 14 (6) ◽  
pp. 1351-1380
Author(s):  
Sakthivel V.P. ◽  
Suman M. ◽  
Sathya P.D.
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Research Work ◽  
Superior Performance ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Content Type

Purpose Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants. Design/methodology/approach The suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately. Findings Empirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems. Practical implications This study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning. Originality/value To the best of the authors’ knowledge, this manuscript is the first research work of using SSA approach for solving ELD problems. Consequently, the solution to this problem configures the key contribution of this paper.

scholarly journals Sparse Signal Recovery via ECME Thresholding Pursuits

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Heping Song ◽  
Guoli Wang
Keyword(s):  
Optimization Problems ◽  
Greedy Algorithms ◽  
Experimental Studies ◽  
Superior Performance ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Signal Recovery ◽  
Support Set ◽  
Reconstructed Signal

The emerging theory of compressive sensing (CS) provides a new sparse signal processing paradigm for reconstructing sparse signals from the undersampled linear measurements. Recently, numerous algorithms have been developed to solve convex optimization problems for CS sparse signal recovery. However, in some certain circumstances, greedy algorithms exhibit superior performance than convex methods. This paper is a followup to the recent paper of Wang and Yin (2010), who refine BP reconstructions via iterative support detection (ISD). The heuristic idea of ISD was applied to greedy algorithms. We developed two approaches for accelerating the ECME iteration. The described algorithms, named ECME thresholding pursuits (EMTP), introduced two greedy strategies that each iteration detects a support setIby thresholding the result of the ECME iteration and estimates the reconstructed signal by solving a truncated least-squares problem on the support setI. Two effective support detection strategies are devised for the sparse signals with components having a fast decaying distribution of nonzero components. The experimental studies are presented to demonstrate that EMTP offers an appealing alternative to state-of-the-art algorithms for sparse signal recovery.

New Efficient Evolutionary Algorithm Applied to Optimal Reactive Power Dispatch

2014 ◽  
pp. 321-339
Author(s):  
Provas Kumar Roy
Keyword(s):  
Large Scale ◽  
Reactive Power ◽  
Transmission Loss ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Opposition Based Learning ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Equality And Inequality Constraints ◽  
Reactive Power Dispatch

Evolutionary Algorithms (EAs) are well-known optimization techniques to deal with nonlinear and complex optimization problems. However, most of these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. To overcome this drawback and to improve the convergence rate, this chapter employs Quasi-Opposition-Based Learning (QOBL) in conventional Biogeography-Based Optimization (BBO) technique. The proposed Quasi-Oppositional BBO (QOBBO) is comprehensively developed and successfully applied for solving the Optimal Reactive Power Dispatch (ORPD) problem by minimizing the transmission loss when both equality and inequality constraints are satisfied. The proposed QOBBO algorithm's performance is studied with comparisons of Canonical Genetic Algorithm (CGA), five versions of Particle Swarm Optimization (PSO), Local Search-Based Self-Adaptive Differential Evolution (L-SADE), Seeker Optimization Algorithm (SOA), and BBO on the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems. The simulation results show that the proposed QOBBO approach performed better than the other listed algorithms and can be efficiently used to solve small-, medium-, and large-scale ORPD problems.

scholarly journals An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Fuqing Zhao ◽  
Wenchang Lei ◽  
Weimin Ma ◽  
Yang Liu ◽  
Chuck Zhang
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Search Space ◽  
Multiobjective Evolutionary Algorithms ◽  
Local Optima ◽  
Adaptive Selection ◽  
Convergence Performance ◽  
Evolutionary Operators ◽  
Polynomial Mutation

A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA) is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.

scholarly journals Rethinking the Fourier-Mellin Transform: Multiple Depths in the Camera’s View

2021 ◽  
Vol 13 (5) ◽  
pp. 1000
Author(s):  
Qingwen Xu ◽  
Haofei Kuang ◽  
Laurent Kneip ◽  
Sören Schwertfeger
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Feature Detection ◽  
Mellin Transform ◽  
State Of The Art ◽  
Visual Odometry ◽  
Superior Performance ◽  
Fourier Mellin Transform ◽  
And Robotics ◽  
Better Than

Remote sensing and robotics often rely on visual odometry (VO) for localization. Many standard approaches for VO use feature detection. However, these methods will meet challenges if the environments are feature-deprived or highly repetitive. Fourier-Mellin Transform (FMT) is an alternative VO approach that has been shown to show superior performance in these scenarios and is often used in remote sensing. One limitation of FMT is that it requires an environment that is equidistant to the camera, i.e., single-depth. To extend the applications of FMT to multi-depth environments, this paper presents the extended Fourier-Mellin Transform (eFMT), which maintains the advantages of FMT with respect to feature-deprived scenarios. To show the robustness and accuracy of eFMT, we implement an eFMT-based visual odometry framework and test it in toy examples and a large-scale drone dataset. All these experiments are performed on data collected in challenging scenarios, such as, trees, wooden boards and featureless roofs. The results show that eFMT performs better than FMT in the multi-depth settings. Moreover, eFMT also outperforms state-of-the-art VO algorithms, such as ORB-SLAM3, SVO and DSO, in our experiments.

A many-objective evolutionary algorithm based on vector angle distance scaling

2021 ◽  
Vol 40 (5) ◽  
pp. 10285-10306
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Great Success ◽  
Multi Objective ◽  
Environmental Selection ◽  
Fitness Value ◽  
Vector Angle

In the past two decades, multi-objective evolutionary algorithms (MOEAs) have achieved great success in solving two or three multi-objective optimization problems. As pointed out in some recent studies, however, MOEAs face many difficulties when dealing with many-objective optimization problems(MaOPs) on account of the loss of the selection pressure of the non-dominant candidate solutions toward the Pareto front and the ineffective design of the diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm based on vector guidance. In this algorithm, the value of vector angle distance scaling(VADS) is applied to balance convergence and diversity in environmental selection. In addition, tournament selection based on the aggregate fitness value of VADS is applied to generate a high quality offspring population. Besides, we adopt an adaptive strategy to adjust the reference vector dynamically according to the scales of the objective functions. Finally, the performance of the proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on 52 instances of 13 MaOPs with diverse characteristics. Experimental results show that the proposed algorithm performs competitively when dealing many-objective with different types of Pareto front.

scholarly journals An Environmental Selection and Transfer Learning Based Dynamic Multiobjective Optimization Evolutionary Algorithm

2021 ◽  
Author(s):  
Qiang He ◽  
Zheng Xiang ◽  
Peng Ren
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Transfer Learning ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Environmental Selection ◽  
Dynamic Multiobjective Optimization ◽  
Prediction Strategy

Abstract In recent years, the dynamic multiobjective optimization problems (DMOPs), whose major strategy is to track the varying PS (Pareto Optimal Solution, PS) and/or PF (Pareto Optimal Frontier), caused a great deal of attention worldwide. As a promising solution, reusing of “experiences” to establish a prediction model is proved to be very useful and widely used in practice. However, most existing methods overlook the importance of environmental selection in the evolutionary processes. In this paper, we propose a dynamic multiobjective optimal evolutionary algorithm which is based on environmental selection and transfer learning (DMOEA-ESTL). This approach makes full use of the environmental selection and transfer learning technique to generate individuals for a new environment by reusing experience to maintain the diversity of the population and speed up the evolutionary process. As experimental validation, we embed this new scheme in the NSGA-II (non-dominated sorting genetic algorithm). We test the proposed algorithm with the help of six benchmark functions as well as compare it with the other two prediction based strategies FPS (Forward-looking Prediction Strategy, FPS) and PPS (Population Prediction Strategy, PPS). The experimental results testify that the proposed strategy can deal with the DMOPs effectively.

scholarly journals A New Conjugate Gradient Method with Sufficient Descent Property

2021 ◽  
pp. 163-174
Author(s):  
O.B. Akinduko
Keyword(s):  
Conjugate Gradient ◽  
Large Scale ◽  
Optimization Problems ◽  
Gradient Methods ◽  
Superior Performance ◽  
Sufficient Descent Property ◽  
Unconstrained Optimization Problems ◽  
Descent Property ◽  
Sufficient Descent ◽  
Large Scale Unconstrained Optimization

In this paper, by linearly combining the numerator and denominator terms of the Dai-Liao (DL) and Bamigbola-Ali-Nwaeze (BAN) conjugate gradient methods (CGMs), a general form of DL-BAN method has been proposed. From this general form, a new hybrid CGM, which was found to possess a sufficient descent property is generated. Numerical experiment was carried out on the new CGM in comparison with four existing CGMs, using some set of large scale unconstrained optimization problems. The result showed a superior performance of new method over majority of the existing methods.

scholarly journals Cooperative Coevolution with Two-Stage Decomposition for Large-Scale Global Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
H. D. Yue ◽  
Y. Sun
Keyword(s):  
Global Optimization ◽  
Decomposition Method ◽  
Large Scale ◽  
Optimization Problems ◽  
Decomposition Methods ◽  
Unit Vector ◽  
Cooperative Coevolution ◽  
Two Stage ◽  
Computational Resources ◽  
Dynamic Decomposition

Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems.

Sign in / Sign up

Export Citation Format

Share Document