检索结果-内蒙古大学图书馆

Half a Dozen Real-World Applications of evolutionary Multitasking, and More

IEEE computationAL INTELLIGENCE MAGAZINE 2022年第2期17卷 49-66页

作者： Gupta, Abhishek Zhou, Lei Ong, Yew-Soon Chen, Zefeng Hou, Yaqing ASTAR Singapore Singapore Nanyang Technol Univ NTU Singapore Singapore Nanyang Technol Univ Singapore Singapore ASTAR Singapore Singapore Dalian Univ Technol Dalian Peoples R China

Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population's implicit parallelism to jointly solve a set of tasks, hence creating avenues for skills transfer between them. Despite it being early days, the idea of EMT has begun to show promise in a range of real-world applications. In the backdrop of recent advances, the contribution of this paper is twofold. First, a review of several application-oriented explorations of EMT in the literature is presented;the works are assimilated into half a dozen broad categories according to their respective application domains. Each of these six categories elaborates fundamental motivations to multitask, and contains a representative experimental study (referred from the literature). Second, a set of recipes is provided showing how problem formulations of general interest, those that cut across different disciplines, could be transformed in the new light of EMT. Our discussions emphasize the many practical use-cases of EMT, and are intended to spark future research towards crafting novel algorithms for real-world deployment.

关键词： Scalability Production Parallel processing Performance gain Multitasking Robustness evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Metaheuristics "In the Large"

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期297卷 393-406页

作者： Swan, Jerry Adriaensen, Steven Brownlee, Alexander E. I. Hammond, Kevin Johnson, Colin G. Kheiri, Ahmed Krawiec, Faustyna Merelo, J. J. Minku, Leandro L. ozcan, Ender Pappa, Gisele L. Garcia-Sanchez, Pablo Sorensen, Kenneth Voss, Stefan Wagner, Markus White, David R. Univ York Heslington York YO10 5DD N Yorkshire England Albert Ludwigs Univ Freiburg D-79110 Freiburg Germany Univ Stirling Stirling FK9 4LA Scotland Hylomorph Solut Glasgow G2 4JR Lanark Scotland Univ Nottingham Nottingham NG8 1BB England Univ Lancaster Lancaster LA1 4YW England Univ Cambridge Cambridge CB3 0FD England Univ Granada Granada 18071 ES Spain Univ Birmingham Birmingham B15 2TT W Midlands England Univ Fed Minas Gerais BR-31270010 Belo Horizonte MG Brazil Univ Antwerp Prinsstr 13 B-2000 Antwerp Belgium Univ Hamburg D-20146 Hamburg Germany Univ Adelaide Adelaide SA 5005 Australia Univ Sheffield Sheffield S10 2TN S Yorkshire England

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics "In the Large" project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field's progress. We argue that, via such principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： evolutionary computation Operational Research Heuristic design Heuristic methods Architecture Frameworks Interoperability

来源：评论

学校读者我要写书评

暂无评论

Economic and Emission Dispatch Solution Using evolutionary Mating Algorithm 1

引用

7th International Conference on Electrical, Control, and Computer Engineering, InECCE 2023

作者： Abdul Aziz, Ahmad Shahier Sulaiman, Mohd Herwan Mustaffa, Zuriani Pahang Pekan26600 Malaysia Pahang Pekan26600 Malaysia

ISBN: (数字)9789819738519

ISBN: (纸本)9789819738502

This paper presents the evolutionary Mating Algorithm (EMA) as a novel evolutionary algorithm for addressing economic emission load dispatch (EELD) problems. The optimization of power systems with respect to economic and emission considerations is of utmost importance in contemporary power system engineering. Emphasizing cost and emission reduction is essential for efficient power system operation. In this study, the economic and emission dispatch problem is tackled using the evolutionary Mating Algorithm (EMA). The performance of the EMA algorithm is evaluated on a 10 and 40-unit generator test system. Comparative analyses are conducted with other algorithms, namely the Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA), and Barnacles Mating Optimizer (BMO). The results indicate the effectiveness of the evolutionary Mating Algorithm in solving economic emission dispatch problems, thereby demonstrating the efficacy of the proposed EMA approach for addressing EELD problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Economic emission load dispatch Evolutional mating algorithm evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Indicator-Based evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems

引用

IEEE TRANSACTIONS ON evolutionary computation 2022年第2期26卷 379-391页

作者： Yuan, Jiawei Liu, Hai-Lin Ong, Yew-Soon He, Zhaoshui Guangdong Univ Technol Sch Automat Guangzhou 510520 Peoples R China Guangdong Univ Technol Minist Educ Key Lab IoT Intelligent Informat Proc & Syst Inte Guangzhou 510520 Peoples R China Guangdong Univ Technol Sch Appl Math Guangzhou 510520 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

To prevent the population from getting stuck in local areas and then missing the constrained Pareto front fragments in dealing with constrained multiobjective optimization problems (CMOPs), it is important to guide the population to evenly explore the promising areas that are not dominated by all examined feasible solutions. To this end, we first introduce a cost value-based distance into the objective space, and then use this distance and the constraints to define an indicator to evaluate the contribution of each individual to exploring the promising areas. Theoretical studies show that the proposed indicator can effectively guide population to focus on exploring the promising areas without crowding in local areas. Accordingly, we propose a new constraint handling technique (CHT) based on this indicator. To further improve the diversity of population in the promising areas, the proposed indicator-based CHT divides the promising areas into multiple subregions, and then gives priority to removing the individuals with the worst fitness values in the densest subregions. We embed the indicator-based CHT in evolutionary algorithm and propose an indicator-based constrained multiobjective algorithm for solving CMOPs. Numerical experiments on several benchmark suites show the effectiveness of the proposed algorithm. Compared with six state-of-the-art constrained evolutionary multiobjective optimization algorithms, the proposed algorithm performs better in dealing with different types of CMOPs, especially in those problems that the individuals are easy to appear in the local infeasible areas that dominate the constrained Pareto front fragments.

关键词： Statistics Sociology Optimization evolutionary computation Uninterruptible power systems Search problems Constraint handling Constraint handling evolutionary algorithm indicator multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A Multisize no Migration Island-Based Differential Evolution Algorithm With Removal of Ineffective Islands

引用

IEEE ACCESS 2022年 10卷 34539-34549页

作者： Skakovski, Aleksander Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

The paper is a continuation of our previous research where a novel concept of multisize island model was proposed. Such multisize approach facilitates the design of island-based algorithms and brings such benefits as: improved fitness dynamics throughout the entire time of operation even without the migration of solutions between the islands. The absence of migration eliminates the need to establish the topology and the policy of migration. It also makes the efficiency of multisize island-based algorithms independent of the particular islands' size and eliminates the need of tuning the size of islands which is usually done in the case of the canonical island model. All these features indicate the superiority of the multisize island model over the canonical one. In this paper we improved earlier proposed multisize island-based DE algorithm by adding to it the ability to automatically optimize the number of islands in operation. This feature enables the release of most computational units before the algorithm completes its operation in the case of concurrent execution of the algorithm on multiple computational units, or reduction of the algorithm running time in the case of its execution on a single computational unit. The proposed algorithm was tested by solving computationally difficult scheduling problem, which is the discrete-continuous scheduling with continuous resource discretization.

关键词： Statistics Sociology computational modeling Heuristic algorithms Optimization Topology Indexes evolutionary computation multisize island model differential evolution discrete-continuous scheduling

来源：评论

学校读者我要写书评

暂无评论

A Reference Vector-Based Simplified Covariance Matrix Adaptation Evolution Strategy for Constrained Global Optimization

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3696-3709页

作者： Kumar, Abhishek Das, Swagatam Mallipeddi, Rammohan Indian Inst Technol Varanasi Dept Elect Engn Varanasi 221005 Uttar Pradesh India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India Kyungpook Natl Univ Coll IT Engn Dept Artificial Intelligence Daegu 41566 South Korea

During the last two decades, the notion of multiobjective optimization (MOO) has been successfully adopted to solve the nonconvex constrained optimization problems (COPs) in their most general forms. However, such works mainly utilized the Pareto dominance-based MOO framework while the other successful MOO frameworks, such as the reference vector (RV) and the decomposition-based ones, have not drawn sufficient attention from the COP researchers. In this article, we utilize the concepts of the RV-based MOO to design a ranking strategy for the solutions of a COP. We first transform the COP into a biobjective optimization problem (BOP) and then solve it by using the covariance matrix adaptation evolution strategy (CMA-ES), which is arguably one of the most competitive evolutionary algorithms of current interest. We propose an RV-based ranking strategy to calculate the mean and update the covariance matrix in CMA-ES. Besides, the RV is explicitly tuned during the optimization process based on the characteristics of COPs in a RV-based MOO framework. We also propose a repair mechanism for the infeasible solutions and a restart strategy to facilitate the population to escape from the infeasible region. We test the proposal extensively on two well-known benchmark suites comprised of 36 and 112 test problems (at different scales) from the IEEE CEC (Congress on evolutionary computation) 2010 and 2017 competitions along with a real-world problem related to power flow. Our experimental results suggest that the proposed algorithm can meet or beat several other state-of-the-art constrained optimizers in terms of the performance on a wide variety of problems.

关键词： Optimization Covariance matrices Maintenance engineering Benchmark testing Decision feedback equalizers evolutionary computation Cybernetics Constrained optimization covariance matrix adaptation evolution strategy (CMA-ES) multiobjective optimization (MOO) reference vector-based ranking (RVRanking) strategy

来源：评论

学校读者我要写书评

暂无评论

Solving Bilevel Optimization Problems Using Kriging Approximations

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 10639-10654页

作者： Sinha, Ankur Shaikh, Vaseem Indian Inst Management Ahmedabad Prod & Quantitat Methods Ahmadabad 380006 Gujarat India

Bilevel optimization involves two levels of optimization, where one optimization problem is nested within the other. The structure of the problem often requires solving a large number of inner optimization problems that make these kinds of optimization problems expensive to solve. The reaction set mapping and the lower level optimal value function mapping are often used to reduce bilevel optimization problems to a single level;however, the mappings are not known a priori, and the need is to be estimated. Though there exist a few studies that rely on the estimation of these mappings, they are often applied to problems where one of these mappings has a known form, that is, piecewise linear, convex, etc. In this article, we utilize both these mappings together to solve general bilevel optimization problems without any assumptions on the structure of these mappings. Kriging approximations are created during the generations of an evolutionary algorithm, where the population members serve as the samples for creating the approximations. One of the important features of the proposed algorithm is the creation of an auxiliary optimization problem using the Kriging-based metamodel of the lower level optimal value function that solves an approximate relaxation of the bilevel optimization problem. The auxiliary problem when used for local search is able to accelerate the evolutionary algorithm toward the bilevel optimal solution. We perform experiments on two sets of test problems and a problem from the domain of control theory. Our experiments suggest that the approach is quite promising and can lead to substantial savings when solving bilevel optimization problems. The approach is able to outperform state-of-the-art methods that are available for solving bilevel problems, in particular, the savings in function evaluations for the lower level problem are substantial with the proposed approach.

关键词： Optimization evolutionary computation computational modeling Approximation algorithms Linear programming Control theory Tuning Bilevel optimization evolutionary algorithm Kriging

来源：评论

学校读者我要写书评

暂无评论

A Rough-to-Fine evolutionary Multiobjective Optimization Algorithm

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13472-13485页

作者： Gu, Fangqing Liu, Hai-Lin Cheung, Yiu-Ming Zheng, Minyi Guangdong Univ Technol Coll Appl Math Guangzhou 510520 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Guangdong Univ Technol Sch Appl Math Guangzhou 510520 Peoples R China

This article presents a rough-to-fine evolutionary multiobjective optimization algorithm based on the decomposition for solving problems in which the solutions are initially far from the Pareto-optimal set. Subsequently, a tree is constructed by a modified k-means algorithm on N uniform weight vectors, and each node of the tree contains a weight vector. Each node is associated with a subproblem with the help of its weight vector. Consequently, a subproblem tree can be established. It is easy to find that the descendant subproblems are refinements of their ancestor subproblems. The proposed algorithm approaches the Pareto front (PF) by solving a few subproblems in the first few levels to obtain a rough PF and gradually refining the PF by involving the subproblems level-by-level. This strategy is highly favorable for solving problems in which the solutions are initially far from the Pareto set. Moreover, the proposed algorithm has lower time complexity. Theoretical analysis shows the complexity of dealing with a new candidate solution is O(M log N), where M is the number of objectives. Empirical studies demonstrate the efficacy of the proposed algorithm.

关键词： Statistics Sociology Optimization evolutionary computation Approximation algorithms Sorting Diversity reception Decomposition evolutionary algorithm incremental multiobjective optimization tree-like weight design

来源：评论

学校读者我要写书评

暂无评论

Exploiting multi-level parallel metaheuristics and heterogeneous computing to boost phylogenetics

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 127卷 208-224页

作者： Santander-Jimenez, Sergio Vega-Rodriguez, Miguel A. Sousa, Leonel Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Campus Univ S-N Caceres 10003 Spain Inst Engn Sistemas & Comp Invest & Desenvolvimen Rua Alves Redol 9 P-1000029 Lisbon Portugal

Optimization problems are becoming increasingly difficult challenges as a result of the definition of more realistic formulations and the availability of larger input data. Fortunately, the computing capabilities of state-of-the-art heterogeneous systems represent an opportunity to deal with the main complexity factors of these problems. These platforms open the door to the definition of robust metaheuristic solvers, in which parallel computations of different nature can be efficiently mapped to the most suitable architectures and hardware resources. This work investigates the combination of multi-level parallelism and heterogeneous computing to address an important multiobjective problem in bioinformatics: phylogenetics. A parallel metaheuristic approach, based on the joint exploitation of parallel tasks at the algorithm, iteration, and solution levels, is proposed to tackle computationally intensive inferences on CPU+GPU systems. Different heterogeneous design alternatives are also discussed, in accordance with the way the interactions between CPU and GPU are handled. The experimental evaluation of the proposal on real-world biological datasets points out the benefits of using multi-level, heterogeneous strategies, reporting accelerations up to 396x over the baseline metaheuristic as well as significant energy savings with regard to other parallel approaches, without impacting multiobjective solution quality. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Heterogeneous computing Multi-level parallelism evolutionary computation High performance computing Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Force-Based Algorithm for Motion Planning of Large Agent

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 654-665页

作者： Semnani, Samaneh Hosseini de Ruiter, Anton H. J. Liu, Hugh H. T. Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Ryerson Univ Aerosp Engn Dept Toronto ON M5B 2K3 Canada Univ Toronto Fac Appl Sci & Engn Toronto ON M3H 5T6 Canada

This article presents a distributed, efficient, scalable, and real-time motion planning algorithm for a large group of agents moving in 2-D or 3-D spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: 1) collision avoidance and 2) navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm can find collision-free motions with lower transition time, free from velocity state information of neighboring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2-D and 3-D benchmark simulation scenarios, with results outperforming existing methods.

关键词： Planning Heuristic algorithms Trajectory Real-time systems Navigation computational complexity Force Collision avoidance distributed algorithms evolutionary computation flocking algorithm motion planning multiagent systems trajectory optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：