Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Efficient Power Allocation Schemes for Hybrid Decode-Amplify-Forward Relay Based Wireless Cooperative Network

Cooperative communication in various wireless domains, such as cellular networks, sensor networks and wireless ad hoc networks, has gained significant interest recently. In cooperative network, relays between the source and the destination, form a virtual MIMO that creates spatial diversity at the destination, which overcomes the fading effect of wireless channels. Such relay assisted schemes have potential to increase the channel capacity and network coverage. Most current research on cooperative communication are focused broadly on efficient protocol design and analysis, resource allocation, relay selection and cross layer optimization. The first part of this research aims at introducing hybrid decode-amplify-forward (HDAF) relaying in a distributed Alamouti coded cooperative network. Performance of such adaptive relaying scheme in terms of symbol error rate (SER), outage probability and average channel capacity is derived theoretically and verified through simulation based study. This work is further extended to a generalized multi HDAF relaying cooperative frame work. Various efficient power allocation schemes such as maximized channel capacity based, minimized SER based and total power minimization based are proposed and their superiority in performance over the existing equal power allocation scheme is demonstrated in the simulation results. Due to the broadcast nature of wireless transmission, information privacy in wireless networks becomes a critical issue. In the context of physical layer security, the role of multi HDAF relaying based cooperative model with control jamming and multiple eavesdroppers is explored in the second part of the research. Performance evaluation parameters such as secrecy rate, secrecy outage and intercept probability are derived theoretically. Further the importance of the proposed power allocation schemes in enhancing the secrecy performance of the network in the presence of multiple eavesdroppers is studied in detail through simulation based study and analysis. For all the proposed power allocation schemes in this research, the optimization problems are defined under total power constraint and are solved using Lagrange multiplier method and also evolutionary algorithms such as Differential evolution and Invasive Weed Optimization are employed. Monte Carlo simulation based study is adopted throughout the research. It is concluded that HDAF relaying based wireless cooperative network with optimal power allocation schemes offers improved and reliable performance compared to conventional amplify forward and decode forward relaying schemes. Above research contributions will be applicable for future generation wireless cooperative networks

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research