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This simple idea, which is somewhat standard in computer science, allows to open up new research perspectives and challenges in both the fundamental level of our understanding of multi-objective problems and concerning designing and implementing new efficient algorithms for solving them.

Evolutionary Algorithms, - Prospectus - Universiteit Leiden

Many different DMOEAs variants have been proposed, studied and applied to various application domains in recent years. However, DMOEAs are still in their very early infancy, since only a few basic design principles have been established compared to the vast body of literature dedicated to other well-established approaches e.

Thus, the topics of interest include but are not limited to the following aspects:. Locally, populations called fireworks exploit local landscape by a simple sampling method called explosion operation. Globally, fireworks exchange condensed information and collaboratively decide parameters of their explosion. FWA achieved overwhelming success on both benchmark objective functions and real-world problems.

Recent research includes many effective variants and huge amount of successful applications. FWA framework has revealed competitive performance with other SI optimization methods. We are expecting researches on theoretical analysis and improvement of FWA and application of all kinds of practical situations. Full papers are invited on recent advances in the development of FWA.

The session seeks to promote the discussion and presentation of novel works related but not limited to the following issues:. Swarm intelligence, as a crucial aspect of the artificial intelligence domain, has become an increasingly important modern computational intelligence method in artificial intelligence and computer science. In swarm intelligence, the nascent collective intelligence of groups of simple agents possess a powerful global search capability, and has been demonstrated to be able to determine the optimal solution within a rational time by numerous study fields using swarm intelligence algorithms, such as GA, MA, ACO, PSO, ABC, SSO, etc.

Swarm intelligence algorithms play a paramount role in optimizing the increasing problems in related complex systems. Despite a significant amount of research on Swarm Intelligence, there remain many open issues and intriguing challenges in the field. This special session will provide a cardinal opportunity to present the latest scientific results and methods on the collaboration of Swarm Intelligence in Operations Research, Management Science and Decision Making, to discuss and exchange the latest developments in Swarm Intelligence, and to explore the future directions in Swarm Intelligence.

Authors are invited to submit their original and unpublished work in the areas including, but not limited to:.


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Smart logistics refers to the efficient and effective design, planning and control of the supply chain processes though intelligent technologies, such as software to improve the design of networks, software to automate scheduling, routing, and dispatching, material handling systems, etc. Respectively, the relevant research methods involve clustering, stochastic dual dynamic programming, planning and optimization.

In recent years, evolutionary computation EC techniques have been introduced to the area of logistics. Examples include applying single-objective and multi-objective evolutionary algorithms to facility layout decision problems and vehicle routing problems.

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This special session aims at presenting the latest research on EC applications to logistics. Real-world applications of EC on logistics are highly recommended.

The topics include but are not limited to:. By bringing together multiple energy systems, including electricity, thermal sources and fuels, and other critical infrastructures, such as transportation, water and communication, we can improve their efficiency, reliability and resiliency.

Currently, most energy systems and critical infrastructures are operated independently. Multiple energy systems integration MESI focus on the coordination and optimization of those energy systems and critical infrastructures in the operation and planning stages. Various operating conditions, include normal operations, typical interruptions and extreme events, are considered to maximize the value of each unit of energy we use and enhance the ability of those systems and infrastructures to withstand and recover from typical and catastrophic disturbances.

Most optimization problems we encounter in MESI could be nonconvex and contain large number of integer variables which cannot be solved efficiently using existing mathematic programming methods. The fields of computer vision CV and image processing IP have tried to automate tasks that the human visual system can do, with the aim of gaining a high-level understanding of images and videos. CV algorithms have been successfully applied to a large number of real-world problems ranging from remote sensing to medical image analysis, video surveillance, human-robot interaction, and computer-aided design.

In turn, evolutionary computation EC methods have shown to be more efficient than classical optimization approaches for discontinuous, non-differentiable, multimodal and noisy problems.

IEEE CEC 12222 Special Sessions

The scope of this special session covers, but is not limited to, the application of EC paradigms to:. Evolvable systems encompass understanding, modelling and applying biologically inspired mechanisms to physical systems. Having showcased examples from analogue and digital electronics, antennas, MEMS chips, optical systems, carbon nanotubes as well as quantum circuits in the past, we are looking for papers that apply techniques and applications of evolvable systems to these hardware systems.

Within the scope of this special session are Evolutionary Systems for Semiconductor Design, Simulation and Fabrication.

Evolutionary Robotics and Machine Learning. Evolutionary Computing Systems for Artificial Intelligence. Evolutionary Substrates for Unconventional Computing. Topics include:.

During last years, a wide range of population-based meta-heuristics have been proposed with the aim of dealing not only with benchmark optimization problems, but also with real-world applications. Population-based approaches keep a set of solutions with the aim of exploring the search space in an efficient way.

Usually, a diverse set of solutions is maintained, meaning that several regions are explored simultaneously. However, one common problem of population-based meta-heuristics is that for some test cases they might exhibit a tendency to converge quickly towards some regions. One of the most frequent problems that these types of meta-heuristics have to deal with is premature convergence, which arises when every member of the population is located at a sub-optimal area of the decision space from where they cannot escape. A significant number of methods have been proposed in order to preserve the diversity in a set of solutions.

This special session aims to attract the most relevant advances produced in the following topics, including but not limited to:. Topics such as the ones of multi-objective process optimisation, decision-making, real-time performance optimisation are pivotal for the realisation of the Industry 4.

Conference on Evolutionary Computation Theory and Applications (ins) AS

Whether the manufacturing application is about digitisation systems, robotics, digital manufacturing or fundamental understanding of advanced processes and complex materials, evolutionary optimisation is ideally placed to offer algorithms and methods to address challenges specific to the manufacturing sector.

In this session, we invite contributions that demonstrate new applications, results, as well as new algorithms that address challenges specific to advanced manufacturing systems. Specific topics of interest include, but not limited to:. Quantum computing QC represents a broad topic encompassing a large number of approaches, technologies and techniques focusing on the usage, application, design and understanding of quantum computing systems.

Evolutionary Computing EC has been on several occasions directly linked to quantum computing such as quantum evolutionary computation or evolutionary design for quantum computer design, etc. Because quantum computing evolves in the very large Complex Hilbert space, evolutionary methods are a prime tool for exploration and exploitation of quantum properties.

The aim of this special session on quantum and evolutionary computing is to provide a platform for researchers of various fields to discuss the latest advances in related fields, technologies and approaches linking and using quantum and evolutionary approaches. The scope of this special session covers among others but not limited to the following topics:. Research work is welcome concerning complex real-world applications of evolutionary computation EC in the energy domain.

The problems can be focused on different parts of the energy chain e. Problems dealing with uncertainty, dynamic environments, many-objectives, and large-scale search spaces are important for the scope of this special session. This special session aims at bringing together the latest applications of EC to complex optimization problems in the energy domain. Therefore, participants are also welcome to submit the results of their algorithm to our session.

Topics must be related to EC in the energy domain including, but not limited to:. Transportation serves as an important task in modern human life and industry activities. Optimization for intelligent transportation systems has shown to be a difficult problem. The worldwide division of labor, the connection of distributed centers, and the increased mobility of individuals, furthermore, lead to an increased demand for efficient solutions to solve the problems in transportation networks.

Evolutionary computation plays a significant role and has gained promising results in optimization of transportation networks. The aim of this special session is to promote research and reflect the most recent advances of evolutionary computation in in intelligent transportation systems. From global trajectory optimization to multidisciplinary aircraft and spacecraft design, from planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes or satellites, computational intelligence CI techniques have become an important — and in many cases inevitable — tool for tackling these kinds of problems, providing useful and non-intuitive solutions.

This special session intends to collect many, diverse efforts made in the application of computational intelligence techniques, or related methods, to aerospace problems. In particular, evolutionary methods specifically devised, adapted or tailored to address problems in space and aerospace applications or evolutionary methods that were demonstrated to be particularly effective at solving aerospace related problems are welcome.

Evolutionary computation EC algorithms have aroused great attentions from both the academic and industrial communities in recent years due to their promising performance in many real-world optimization problems. In order to promote traditional centralized EC algorithms to solve the complicated optimization problems in big data era, using distributed technology to enhance EC algorithms is a promising approach.