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 s...
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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 evolutionaryalgorithm (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.
The increasing complexity of real-world problems raises new challenges to evolutionary computation. distributed models have been successfully employed by many evolutionaryalgorithms (EAs) to deal with these challenge...
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The increasing complexity of real-world problems raises new challenges to evolutionary computation. distributed models have been successfully employed by many evolutionaryalgorithms (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 evolutionaryalgorithms. 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
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...
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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).
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 indus...
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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.
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.
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...
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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.
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.
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.
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.
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