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

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies

The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance

Big data optimization in electric power systems: a review

There are different definitions of big data, and among them, the most common definition refers to three or five characteristics, called volume, velocity, variety, value, and veracity from (Laney (2001)). Volume could include Tera Byte, Peta Byte, Exa Byte, and Zetta Byte. Velocity describes how fast the data are retrieved and processed ‘‘Batch or streaming”. Variety describes structured, semi-structured, and unstructured data (Laney, 2001, Zikopoulos and Eaton, 2011). Veracity explains the integrity and disorderliness of data, while value refers to how good is the “value” we derive from analyzing data? (Zicari et al., 2016). Electrical power systems are networks of components arrayed to supply, transfer, and use electric power. In power system since models are used to predict and characterize operations. However, there is a necessity for powerful optimization algorithms for information processing to learn models as the size increase of data is becoming a global problem to solve large-scale optimization problems. Any optimization problem includes a real function to be maximized or minimized by systematically determination of input values from an allowed set of values. Richness and quantity of large data sets provide the potential to enhance statistical learning performance but require smart models that use the latent low-dimensional structure for effective 2 data separation. This chapter reviews the most recent scientific articles related to large and big data optimization in power systems. Optimization issues such as logistics in power systems and techniques including nonsmooth, nonconvex, and unconstrained large-scale optimization are presented. After a brief review of big data, scientometric analysis has been applied using keywords of “big data” and “power system.” Besides, keywords analysis, network visualization, journal map, and bibliographic coupling analysis have been done to draw a path on big data works in power system problems. Also, the most common useful techniques in large-scale optimization in power system have been reviewed. At the end of this chapter, metaheuristic techniques in big data optimization are reviewed to show that many efforts have been involved in big data optimization in power system and systematically highlight some perspectives on big data optimization

Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

[EN] Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are ATP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed MD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.Pedro Alfaro-Fernandez and Ruben Ruiz are partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds and under grants BES-2013-064858 and EEBB-I-15-10089. This work was supported by the COMEX project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stiitzle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director.Alfaro-Fernandez, P.; Ruiz García, R.; Pagnozzi, F.; Stützle, T. (2020). Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems. 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An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Applied Soft Computing, 12(6), 1755-1764. doi:10.1016/j.asoc.2012.01.011Lopez-Ibanez, M., & Stutzle, T. (2012). The Automatic Design of Multiobjective Ant Colony Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 16(6), 861-875. doi:10.1109/tevc.2011.2182651López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016). The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives, 3, 43-58. doi:10.1016/j.orp.2016.09.002Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems. IEEE Transactions on Evolutionary Computation, 18(2), 301-305. doi:10.1109/tevc.2013.2240304Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). 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Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC