IEEE Congress on Evolutionary Computation 2015 (CEC2015)
May 25 - 28
Sanaz Mostaghim, Otto von Guericke University Magdeburg, Germany
Kalyanmoy Deb, Michigan State University, USA
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 2015 IEEE Congress on Evolutionary Computation (CEC'2015) 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 You are invited to submit papers that are unpublished original work for this special session at CEC 2015. The topics are, but not limited to, the following
Theoretical aspects of EMO algorithms
Real-world applications of EMO algorithms
Test and benchmark problem construction for EMO algorithms
Multiobjectivization and visualization techniques
Innovization and knowledge discovery through EMO
New EMO techniques including those using meta-heuristics such as artificial immune systems, particle swarm optimization, differential evolution, cultural algorithms, etc.
Handling practicalities, such as constraints, uncertainty, noise, dynamically changing multi-objective problems, bi-level multi-objective problems, mixed-integer multi- objective problems, computationally expensive multi-objective problems through meta- modeling, fixed budget of evaluations for multi-objective optimization, large-scale EMO applications, etc.
Performance measures for EMO algorithms including hypervolume estimation
Diversity preserving techniques in EMO
Comparative studies of EMO algorithms and with classical methods
Evolutionary multi-objective combinatorial optimization, EMO control problems, EMO inverse problems, EMO data mining, EMO machine learning
Memetic and Metaheuristics based EMO algorithms
Hybrid approaches combining, for example, EMO algorithms with mathematical programming techniques and exact methods
Parallel EMO approaches
EMO and multi-criterion decision making (MCDM)
Adaptation, learning, and anticipation