This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their streng...
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This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global *** exploration evolves three independent populations by heterogenous *** exploitation evolves an external elite archive in parallel with exploration to balance global and local *** transfer is based on a point-ring communication topology to share successful experiences among distinct search *** restart adopts an adaptive perturbation strategy to control search diversity *** computation is a newly emerging technique,which has powerful computing power and parallelized ***,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionaryalgorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum *** QDMA integrates the superiorities of distributed,memetic,and quantum *** experiments are carried out to evaluate the superior performance of *** results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum *** superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.
evolutionaryalgorithms (EAs) have shown their great capability of handling optimization problems. In the domain of large scale global optimization, many distributed EAs (dEAs) have been proposed for maintaining popul...
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ISBN:
(纸本)9781728121536
evolutionaryalgorithms (EAs) have shown their great capability of handling optimization problems. In the domain of large scale global optimization, many distributed EAs (dEAs) have been proposed for maintaining population diversity so as to enhance their efficacy. One well-known variant of dEA is the island model EA, in which several populations form islands communicating through migration. There are several key factors that affect the performance and search behavior of island model EA, such as population size of each island and migration topology. While most studies of dEA focus on low or medium dimensional problems, an investigation into the effects of these factors on high dimensional problems is greatly needed. This study presents an empirical analysis of island model EA on large scale global optimization problems. The analysis examines the solution quality, convergence speed, and population diversity of island model EA with different migration topologies, population sizes, migration rates, and migration frequencies on four benchmark function of 1,000 dimensions. The results render guidelines for using island model EA to solve large scale global optimization problems.
The concept of channel, a computational mechanism used to convey state to different threads of process execution, is at the core of the design of multi-threaded concurrent algorithms. In the case of concurrent evoluti...
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ISBN:
(纸本)9783030310196;9783030310189
The concept of channel, a computational mechanism used to convey state to different threads of process execution, is at the core of the design of multi-threaded concurrent algorithms. In the case of concurrent evolutionaryalgorithms, channels can be used to communicate messages between several threads performing different evolution tasks related to genetic operations or mixing of populations. In this paper we study to what extent the design of these messages in a communicating sequential process context may influence scaling and performance of concurrent evolutionaryalgorithms. For this aim, we designed a channel-based concurrent evolutionaryalgorithm that is able to effectively solve different benchmark binary problems (e.g. OneMax, LeadingOnes, RoyalRoad), showing that it provides a good basis to leverage the multithreaded and multi-core capabilities of modern computers. Although our results indicate that concurrency is advantageous to scale-up the performance of evolutionaryalgorithms, they also highlight how the trade-off between concurrency, communication and evolutionary parameters affect the outcome of the evolved solutions, opening-up new opportunities for algorithm design.
This paper addresses the problems of autonomous task assignment and path planning for a fleet of heterogeneous unmanned aerial vehicles in cooperative *** previous work many algorithms almost run on centralized archit...
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ISBN:
(纸本)9781509046584
This paper addresses the problems of autonomous task assignment and path planning for a fleet of heterogeneous unmanned aerial vehicles in cooperative *** previous work many algorithms almost run on centralized architecture and handle the task assignment decoupling with the path *** may result in poor ***,this paper investigates a novel integrated solution for UAV to perform multiple consecutive tasks cooperatively on multiple ground targets based on distributed planning *** a given scenario,the heterogeneous vehicles have different capabilities,kinematic constraints,and fuel ***,the task has other constraints,such as task execution orders,UAV conflict free constraints,*** paper presents details of the non-decoupling solution which produces optimal assignment and trajectories for several given *** performance of the algorithm is compared to that of some previous methods in real-time simulation *** simulations results show the viability of the non-decoupling approach,and the non-decoupling solution has an advantage over hierarchical algorithms,and the distributed architecture improves the operation efficiency of the algorithm and the robustness of the UAV.
This paper presents a study of different models for the best individual's growth curve and the takeover time in a distributed evolutionary algorithm (dEA). The calculation of the takeover time is a common analytic...
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ISBN:
(纸本)0780393635
This paper presents a study of different models for the best individual's growth curve and the takeover time in a distributed evolutionary algorithm (dEA). The calculation of the takeover time is a common analytical approach to measure the selection pressure of an EA. This work is another step forward to mathematically unify and describe the roles of several parameters of the migration policy: the migration rate, the migration frequency, and the topology in the selection pressure induced by the dynamics of dEAs. In order to achieve these goals we comparatively evaluate the appropriateness of the well-known panmictic logistic model, hypergraph model and two new models for dEAs. We introduce here new accurate models for growth curves and takeover times in dEAs, and analytically explain the effects of the migration rate, migration frequency, and topology.
Community detection has arisen as an important topic of many different research areas such as sociology, biology and computer science. However, with the appearance of "big data" and the rapid increasing size...
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ISBN:
(纸本)9781728160924
Community detection has arisen as an important topic of many different research areas such as sociology, biology and computer science. However, with the appearance of "big data" and the rapid increasing size of real-world networks, a traditional evolutionaryalgorithm is unable or inefficient to solve the community detection problem in large-scale networks. In this paper, a distributed multi-objective evolutionaryalgorithm (DMOCD) for community detection is proposed. DMOCD is implemented based on Apache Spark and Resilient distributed Datasets. The proposed distributed framework maintains a set of evolving populations (sub-populations) which evolve separately with different crossover and mutation parameters and an external repository as an elite archive to store the non-dominated individuals. A label propagation-based initialization method, a segmented crossover and mutation tactic for large-scale network are introduced. Experiments on both artificial and real-world networks prove that the proposed method is effective for community detection problems in small-scale networks and is able to process the large-scale networks which the stand-alone multi-objective evolutionaryalgorithms cannot deal with.
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