Organizers:Sanaz Mostaghim, University of Magdeburg, Germany email@example.com
Kalyanmoy Deb, Michigan State University, USA, firstname.lastname@example.org
Scope:This special session invites papers discussing recent advances in the development and application of biologically-inspired multi-objective optimization algorithms.
Many problems from science and industry have several (and normally conflicting) objectives that have to be optimized at the same time. Such problems are called multi-objective optimization problems and have been subject of research in the past two decades. One of the reasons why evolutionary algorithms are so suitable for multi-objective optimization is because they can generate a whole set of solutions (the Pareto-optimal solutions) in a single run rather than requiring an iterative one-solution-at-a-time process as followed in traditional mathematical programming techniques.
The main aim of this special session organized within the 2014 IEEE Congress on Evolutionary Computation (CEC'2014) is to bring together both experts and new-comers working on Evolutionary Multi-objective Optimization (EMO) to discuss new and exciting issues in this area.
We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are interested in application papers discussing the power and applicability of these novel methods to real-world problems in different areas in science and industry. You are invited to submit papers that are unpublished original work for this special session at CEC 2014. The topics are, but not limited to, the following
- Many-objective optimization
- Theoretical aspects of EMO algorithms
- Real-world applications of EMO algorithms
- Test and benchmark problems for EMO algorithms
- New EMO techniques including those using meta-heuristics such as artificial immune systems, particle swarm optimization, differential evolution, cultural algorithms, etc.
- Multi-objectivization and visualization techniques
- Handling practicalities, such as uncertainty, noise, constraints, dynamically changing problems, bi-level problems, mixed-integer problems, computationally expensive problems, fixed budget of evaluations, etc.
- Performance measures for EMO algorithms
- Techniques to keep diversity in the population
- Comparative studies of EMO algorithms
- Memetic and Metaheuristics based EMO algorithms
- Hybrid approaches combining, for example, EMO algorithms with mathematical programming techniques and exact methods
- Parallel EMO approaches
- Adaptation, learning, and anticipation
- Evolutionary multi-objective combinatorial optimization, EMO control problems, EMO inverse problems, EMO data mining, EMO machine learning
Author's ScheduleFor the deadline for submitting papers, please check the website of WCCI 2014: