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IEEE Congress on evolutionary Computation (CEC)

作者： Hijaze, Muhannad Corne, David Heriot Watt Univ Sch MACS Edinburgh Midlothian Scotland

ISBN: (纸本)9781424478354

distributed evolutionary algorithms are of increasing interest and importance for three main reasons: (i) a well designed dEA can outperform a 'standard' EA in terms of reliability, solution quality, and speed;(ii) they can (of course) be implemented on parallel hardware, and hence combine efficient utilization of parallel resources with very fast and reliable optimization;(iii) parallel hardware resources are increasingly common. A dEA operates as separate evolving populations with occasional interaction between them via 'migration'. A specific dEA is characterized by the topology and nature of these interactions. The performance of alternative topologies and migration mechanisms in this field remains under-explored. In this paper we continue an investigation of two simple, novel dEA topologies, comparing with the cube-based topology that underpins Alba et al's GD-RCGA (a state of the art dEA). The focus in this paper is on testing a novel adaptive migration scheme, in which the frequency of migration events adapts dynamically in response to the current balance between exploration and exploration. We also focus on high dimensional versions of a selection of hard function optimization problems. We find that the adaptive migration scheme is promising, and that overall results marginally favour a simple three-level tree-based topology and adaptive migration with a longer window, especially as dimensionality increases.

关键词： component distributed evolutionary algorithm function optimization

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Large scale flexible scheduling optimization by a distributed evolutionary algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 894-904页

作者： Sun, Lu Lin, Lin Li, Haojie Gen, Mitsuo Dalian Univ Technol Sch Software Dalian Peoples R China Fuzzy Log Syst Inst Fukuoka Fukuoka Japan Tokyo Univ Sci Tokyo Japan Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China

As a typical combinational optimization problem, the scheduling problem widely exists in many real-world manufacturing industry applications. With the intensification of marketing competition, the increasing problem scale results in the huge exponentially solution space which leads to the unacceptable storage space and computation time delay. In this paper, we consider the large scale flexible scheduling problem and treat the expectation of makespan as the objective function. A distributed cooperative evolutionary algorithm (dcEA) applied on Apache Spark is proposed. First, the dcEA adopts dimension-based distributed model to decompose the population into several sub-populations lengthways and randomly. Second, the dcEA defines resilient distributed dataset (RDD) as sub-populations and performs the identical evolutionary optimization process for all RDDs. Then, the hdEA updates the global best solution by the improved cooperative co-evolution framework. As a typical and basic scheduling problem, 10 benchmarks and three super large scale instances of flexible job shop scheduling are adopted and tested to prove the superiority of proposed dcEA. The numerical results show that dcEA has better performance and lower computational complexity.

关键词： distributed evolutionary algorithm Flexible scheduling Apache Spark Large scale optimization

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Optimization and defect identification using distributed evolutionary algorithms

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2004年第4期17卷 337-344页

作者： Burczynski, T Kus, W Dlugosz, A Orantek, P Silesian Tech Univ Dept Strength Mat & Computat Mech PL-44100 Gliwice Poland Cracow Univ Technol Artificial Intelligence Sect Inst Comp Modelling PL-31155 Krakow Poland

The aim of the paper is to present the application of the distributed evolutionary algorithms to selected optimization and defect identification problems. The coupling of evolutionary algorithms with the finite element method and the boundary element method creates a computational intelligence technique that is very suitable in computer aided optimal design. Several numerical examples for shape, topology optimization and identification are presented for elastic, thermoelastic and elastoplastic structures. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： distributed evolutionary algorithm coevolution optimization identification FEM BEM

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Biological invasion-inspired migration in distributed evolutionary algorithms

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INFORMATION SCIENCES 2012年 207卷 50-65页

作者： De Falco, I. Della Cioppa, A. Maisto, D. Scafuri, U. Tarantino, E. Natl Res Council Italy Inst High Performance Comp & Networking I-80131 Naples Italy Univ Salerno DIEII Nat Computat Lab I-84084 Fisciano SA Italy

Migration strategy plays an important role in designing effective distributed evolutionary algorithms. In this work, a novel migration model inspired to the phenomenon known as biological invasion is devised. The migration strategy is implemented through a multistage process involving invading subpopulations and their competition with native individuals. Such a general approach is used within a stepping-stone parallel model adopting Differential Evolution as the local algorithm. The resulting distributed algorithm is evaluated on a wide set of classical test functions against a large number of sequential and other distributed versions of Differential Evolution available in literature. The findings show that, in most of the cases, the proposed algorithm is able to achieve better performance in terms of both solution quality and convergence rate. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Massive migration Biological invasion distributed evolutionary algorithm

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Framework for distributed evolutionary algorithms in Computational Grids

Framework for Distributed Evolutionary Algorithms in Computa...

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5th International Symposium on Intelligence Computation and Applications

作者： Limmer, Steffen Fey, Dietmar Univ Erlangen Nurnberg Dept Comp Sci 3 D-91058 Erlangen Germany

ISBN: (纸本)9783642164927

In the recent years an increasing number of computational grids have been built, providing an unprecedented amount of computational power. Based on their inherent parallelism, evolutionary algorithms are well suited for distributed execution in such grids. Unfortunately, there are several challenges concerning the usage of a grid infrastructure (e.g. the synchronization and submission of jobs and file transfer tasks). In this paper we present a new framework which makes a Globus based grid easily accessible for evolutionary algorithms and takes care of the parallelization. The usability is demonstrated by the example of an evolutionary algorithm for the Traveling Salesman Problem.

关键词： distributed evolutionary algorithm Computational Grid Globus Toolkit Traveling Salesman Problem

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Subpopulation Diversity Based Accepting Immigrant in distributed evolutionary algorithms

Subpopulation Diversity Based Accepting Immigrant in Distrib...

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19th IEEE International Conference on Parallel and distributed Systems (ICPADS)

作者： Li, Chengjun Wang, Jian Yan, Xianbin Hu, Guangdao China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Fac Earth Resource Wuhan 430074 Peoples R China

ISBN: (纸本)9781479920815

As a popular type of parallel evolutionary algorithms, distributed evolutionary algorithms (DEAs) are widely used in a variety of fields. To get better solutions of concrete problems, a scheme of subpopulation diversity based accepting immigrant in DEAs is proposed in this paper. In migration with this scheme, an immigrant will be put into its target subpopulation only if its current diversity is lower than a threshold value. algorithm analysis shows that the extra cost of time for this scheme is acceptable in many DEAs. Experiments are conducted on instances of the Traveling Salesman Problem from the TSPLIB. Results show that the DEA based on the proposed scheme can get better solutions than the one without it.

关键词： distributed evolutionary algorithm migration occasion subpopulation diversity accepting immigrant threshold value

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A diversity-driven migration strategy for distributed evolutionary algorithms

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SWARM AND evolutionary COMPUTATION 2023年 82卷

作者： Araujo, Jean N. R. Batista, Lucas S. Univ Fed Minas Gerais Grad Program Elect Engn Operat Res & Complex Syst Lab Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Engn Elect Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

The increasing complexity of real-world problems raises new challenges to evolutionary computation. distributed models have been successfully employed by many evolutionary algorithms (EAs) to deal with these challenges. In particular, distributed models provide a means to enable collaboration between multiple subpopulations, thus allowing the design of strategies to deal with premature convergence and loss of diversity, which are common problems in traditional evolutionary algorithms. Through introducing periodic migrations, many distributed evolutionary algorithms (DEAs) have been proposed to improve the balance between exploration and exploitation. However, most of them focus on performing migrations at fixed or probabilistic intervals. In this work, we present a mechanism to estimate the moment of executing the migrations by assessing the loss of diversity of the subpopulations. Another relevant issue is that most studies choose to migrate the best or a random individual. We report a strategy that identifies a migrant individual capable of generating diversity that helps a given subpopulation explore non-visited regions without harming its health. The proposed approach uses an online clustering algorithm to create clouds of good fitness individuals that have been previously migrated. The solution to be migrated must be extracted from a cloud whose population distribution is sufficiently different from the population distribution of the original subpopulation. We called this approach a Diversity-driven Migration Strategy (DDMS). The efficiency of DDMS is experimentally compared against traditional migration strategies (fixed and probabilistic) on the CEC'2014 test suite. Considering the average error values for the objective function, the proposed approach is specially better in 50D and 100D (dimensional) instances. Regarding the diversity, the proposed strategy is better in 100% and about 96% of the test functions in 50D and 100D scenarios, respectively. In gener

关键词： distributed evolutionary algorithm Exploration and exploitation Migratory policy Migratory frequency Online clustering algorithm TEDA

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Online distributed evolutionary optimization of Time Division Multiple Access protocols

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 211卷

作者： Yaman, Anil van der Lee, Tim van Iacca, Giovanni Univ Amsterdam Dept Comp Sci Vrije NL-1081 HV Amsterdam Netherlands Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

With the advent of cheap, miniaturized electronics, ubiquitous networking has reached an unprecedented level of complexity, scale and heterogeneity, becoming the core of several modern applications such as smart industry, smart buildings and smart cities. A crucial element for network performance is the protocol stack, namely the sets of rules and data formats that determine how the nodes in the network exchange information. A great effort has been put to devise formal techniques to synthesize (offline) network protocols, starting from system specifications and strict assumptions on the network environment. However, offline design can be hard to apply in the most modern network applications, either due to numerical complexity, or to the fact that the environment might be unknown and the specifications might not available. In these cases, online protocol design and adaptation has the potential to offer a much more scalable and robust solution. Nevertheless, so far only a few attempts have been done towards online automatic protocol design. These approaches, however, typically require a central coordinator, or need to build and update a model of the environment, which adds complexity. Here, instead, we envision a protocol as an emergent property of a network, obtained by an environment-driven distributed Hill Climbing (DHC) algorithm that uses node-local reinforcement signals to evolve, at runtime and without any central coordination, a network protocol from scratch, without needing a model of the environment. We test this approach with a 3-state Time Division Multiple Access (TDMA) Medium Access Control (MAC) protocol and we observe its emergence in networks of various scales and with various settings. We also show how DHC can reach different trade-offs in terms of energy consumption and protocol performance.

关键词： distributed evolutionary algorithm Network protocol Online adaptation Time Division Multiple Access Multi-objective optimization

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Exploring dynamic population Island genetic algorithm for solving the capacitated vehicle routing problem

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MEMETIC COMPUTING 2024年第2期16卷 179-202页

作者： Rezaei, Babak Gadelha Guimaraes, Frederico Enayatifar, Rasul C. Haddow, Pauline Univ Fed Minas Gerais UFMG Dept Elect Engn Belo Horizonte Brazil Islamic Azad Univ Dept Comp Engn Firoozkooh Branch Tehran Iran Norwegian Univ Sci & Technol NTNU Dept Comp Sci Trondheim Norway

The Vehicle Routing Problem (VRP) holds significant importance in operational research as it deals with optimizing the delivery routes of vehicles to efficiently serve a set of customers. One well-known variant of VRP is the Capacitated Vehicle Routing Problem (CVRP), where the objective is to determine a set of routes for a fleet of identical vehicles, starting and ending at a central depot, while respecting capacity constraints and minimizing total distance traveled. This paper introduces a novel hybrid metaheuristic, named Dynamic Population Island GA and Hybrid Genetic Search (DPIGA-HGS), to tackle the CVRP. DPIGA-HGS combines the strengths of the proposed Dynamic Population Island GA (DPIGA) and Hybrid Genetic Search (HGS) as its local search engine within each island. DPIGA is a specialized variant of Island Genetic algorithm (IGA) that allows islands to lose their populations over time. In the work herein, DPIGA-HGS is shown to outperform existing state-of-the-art algorithms from the literature. It achieves higher quality solutions, leading to a notable increase in the number of Best-Known Solutions (BKS) found and reduced average and maximum solution gaps compared to BKS. The algorithm's effectiveness is demonstrated through several experiments on diverse benchmark instances, including classical benchmarks (Uchoa, CMT, and Golden) and real-world application instances (LoggiBUD).

关键词： distributed evolutionary algorithm Hybrid Genetic Search Capacitated Vehicle Routing Problem Island Genetic Model

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Online evolution for cooperative behavior in group robot systems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2008年第2期6卷 282-287页

作者： Lee, Dong-Wook Seo, Sang-Wook Sim, Kwee-Bo Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

关键词： cooperative behavior distributed evolutionary algorithm distributed mobile robot system dxperience-based crossover Q-learning reinforcement learning

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