Efficient Evolutionary Algorithms for Many-objective Optimization

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Most of the time, when we are talking about an improvement of something, in fact, we are interested to minimize or maximize (i.e. optimize) quality or quantity of an entity. Universal examples are maximization of reliability, efficiency, safety, and benefit; or minimization of the pollution, risk, consumed energy, or production time/cost. Now, it is clear why the fingerprint of the optimization is visible in all science and engineering fields, ranging from healthcare to astronomy. In this direction, nature-inspired problem solving methods play a crucial role to efficiently solve complex real-world problems. Evolutionary Algorithms (EAs) are well-known examples inspired from the genetic biology; they employ biological operations such as selection, crossover, and mutation. EAs are pioneers tackling problems which are hard or even impossible to be solved by the conventional methods. For majority of our practical problems, we are faced with two or more (multi) conflicting objectives to optimize simultaneously; such as minimizing cost and maximizing efficiency for a system. The current successful evolutionary multi-objective algorithms have focused on problems with two or three objectives. However, recently, we face with problems which consist of more than three objectives (called many-objective). EAs have demonstrated their niche in solving these problems due to the requirement of finding multiple trade-off solutions for these problems. However, having a number of algorithmic restrictions, these methods were shown to be non-scalable to many-objective problems. These kinds of problems present new challenges for algorithm design and visualization which have not been addressed properly. This research program expects training 3 PhD and 3 MSc students by involving them in the cutting-edge research topics. These topics address the existing restrictions by enhancing various correlated aspects, namely, a) designing computationally fast algorithms, b) utilizing decomposition methods for dividing an conquering the original problem, c) designing tailored processing type (i.e., sequential, distributed, and parallel), d) designing simple and intuitive large-scale data visualization techniques (for better understanding and supporting an interactive computation), and e) designing effective performance metrics. The outcomes of the current research will be beneficial for a wide range of research communities and industrial sectors in Canada which utilize optimization by any means in scheduling, control systems, robotics, data mining, circuits design, communications, bioinformatics, image processing, networking, traffic engineering, etc. The applicant's more than ten years' comprehensive experience in evolutionary computation will play a pivotal role in success of this research program.