My PhD Thesis

I've finally got around to uploading a close-to-final draft of my PhD thesis (defended in early 2015). As the title suggests, the work is focused on describing several enhancements that can help multi-objective evolutionary algorithms (MOEAs) solve computationally-intensive optimization problems in a decent time frame. Large parts of the main novel scientific contributions of this thesis have been disseminated a priori in 7 peer-reviewed articles (3 journals and 4 conferences and edited volumes).

The practical scenarios I've tackled are all related to multi-objective optimizations of electrical drive designs and the very good results I've obtained on them also stem from a close  and very fruitful cooperation with the Linz Center of Mechatronics (LCM) and the Institute of Electrical Drives and Power Electronics (EAL) of the Johannes Kepler University Linz. Hopefully the version I've uploaded doesn't contain too many mistakes (scientific or language wise), but if you find any, please do let me know.

You can download a close-to-final draft of the paper by clicking here or from my Downloads box (Zavoianu Ciprian - PhD Thesis.pdf). The same version of the document can be previewed at the bottom of this post. For the official, final version of the document, please refer to the Johannes Kepler University Library. If you have any questions regarding the work, please don't hesitate to contact me.

For citations please use the following BibTeX reference:

@PhdThesis{Zavoianu2015PhD,

  author = {Alexandru-Ciprian Z\u{a}voianu},

  title = {Enhanced Evolutionary Algorithms for Solving Computationally-Intensive Multi-Objective Optimization Problems},

  school = {Johannes Kepler University Linz, Austria},

  year = {2015},

  month = {January}

}

Paper: Performance Comparison of Generational and Steady-State Asynchronous Multi-Objective Evolutionary Algorithms for Computationally-Intensive Problems

This journal article describes findings in one of the three main research foci I've investigated during my PhD research project. It is an improved and extended version of the conference manuscript prepared for ICAISC 2013. Here is the abstract of the article:

In the last two decades, multi-objective evolutionary algorithms (MOEAs) have become ever more used in scientific and industrial decision support and decision making contexts the require an a posteriori articulation of preference. The present work is focused on a comparative analysis of the performance of two master-slave parallelization (MSP) methods, the canonical generational scheme and the steady-state asynchronous scheme. Both can be used to improve the convergence speed of multi-objective evolutionary algorithms that must use computationally-intensive fitness evaluation functions. Both previous and present experiments show that a correct choice for one or the other parallelization method can lead to substantial improvements with regard to the overall duration of the optimization process. Our main aim is to provide practitioners of MOEAs with a simple but effective method of deciding which MSP option is better given the particularities of the concrete optimization process. This in turn, would give the decision maker more time for articulating preferences (i.e., more flexibility). Our analysis is performed based on 15 well-known MOOP benchmark problems and two simulation-based industrial optimization process from the field of electrical drive design. For the first industrial MOOP, when comparing with a preliminary study, applying the steady state asynchronous MSP enables us to achieve an overall speedup (in terms of total wall-clock computation time) of ≈ 25%. For the second industrial MOOP, applying the steady-state MSP produces an improvement of ≈ 12%. We focus our study on two of the best known and most widely used MOEAs: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2).

You can download the preprint version of the paper by clicking here or from my Downloads box (Performance Comparison of Generational and Steady-State Asynchronous Multi-Objective Evolutionary Algorithms for Computationally-Intensive Problems - KBS 2015.pdf). The same preprint version can be previewed at the bottom of this post. The final publication is available at elsevier.com.

For citations please use the following BibTeX reference:

@ARTICLE{Zavoianu2015KBS,
  author = {Alexandru-Ciprian Z\u{a}voianu and Edwin Lughofer and Werner Koppelst\"{a}tter and G\"{u}nther Weidenholzer and Wolfgang Amrhein and Erich Peter Klement},
  title = {Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems},
  journal = {Knowledge-Based Systems},

  note = {Computational Intelligence Applications for Data Science},
  year = {2015},
  volume = {87},
  pages = {47-60},
  doi = {10.1016/j.knosys.2015.05.029}
}

Paper: DECMO2 - A Robust Hybrid and Adaptive Multi-Objective Evolutionary Algorithm

This journal article also stems from my PhD research project - enhancing currently available Evolutionary Computation methods employed for solving computationally-intensive multi-objective optimization problems. The presented algorithm - DECMO2 - is an improved version of a method presented in one of our earlier papers. Here is the abstract of the article:

We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wide range of multi-objective optimization problems (MOOPs) as it successfully combines positive traits from three main classes of multi-objective evolutionary algorithms (MOEAs): classical approaches that use Pareto-based selection for survival criteria, approaches that rely on differential evolution, and decomposition-based strategies. A key part of our hybrid evolutionary approach lies in the proposed fitness sharing mechanism that is able to smoothly transfer information between the coevolved subpopulations without negatively impacting the specific evolutionary process behavior that characterizes each subpopulation. The proposed MOEA also features an adaptive allocation of fitness evaluations between the coevolved populations in order to increase robustness and favor the evolutionary search strategy that proves more successful for solving the MOOP at hand. Apart from the new evolutionary algorithm, this paper also contains the description of a new hypervolume and racing-based methodology aimed at providing practitioners from the field of multi-objective optimization with a simple means of analyzing/reporting the general comparative run-time performance of multi-objective optimization algorithms over large problem sets.

You can download the preprint version of the paper by clicking here or from my Downloads box (DECMO2 - A Robust Hybrid and Adaptive Multi-Objective Evolutionary Algorithm - SOCO 2014.pdf). The same preprint version can be previewed at the bottom of this post. The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-014-1308-7.

For citations please use the following BibTeX reference:

@ARTICLE{Zavoianu2014SOCO,
  author = {Alexandru-Ciprian Z\u{a}voianu and Edwin Lughofer and Gerd Bramerdorfer and Wolfgang Amrhein and Erich Peter Klement},
  title = {{DECMO2}: a robust hybrid and adaptive multi-objective evolutionary algorithm},
  journal = {Soft Computing},
  year = {2014},
  volume = {19},
  number = {12},
  pages = {3551-3569},
  doi = {10.1007/s00500-014-1308-7}
}

Paper: An Effective Ensemble-Based Method for Creating On-the-Fly Surrogate Fitness Functions for Multi-Objective Evolutionary Algorithms - SYNASC 2013

This paper was prepared for the SYNASC 2013 conference and it is related to my current research project that has the general aim of enhancing currently available Evolutionary Computation methods employed for the multi-objective optimization of problems that rely on a very time-intensive fitness evaluation functions. Here is the abstract of the article:

The task of designing electrical drives is a multi-objective optimization problem (MOOP) that remains very slow even when using state-of-the-art approaches like particle swarm optimization and evolutionary algorithms because the fitness function used to assess the quality of a proposed design is based on time-intensive finite element (FE) simulations. One straightforward solution is to replace the original FE-based fitness function with a much faster-to-evaluate surrogate. In our particular case each optimization scenario poses rather unique challenges (i.e., goals and constraints) and the surrogate models need to be constructed on-the-fly, automatically, during the run of the evolutionary algorithm. In the present research, using three industrial MOOPs, we investigated several approaches for creating such surrogate models and discovered that a strategy that uses ensembles of multi-layer perceptron neural networks and Pareto-trimmed training sets is able to produce very high-quality surrogate models in a relatively short time interval.

You can download a preliminary version of the paper by clicking here or from my Downloads box (On the Performance of Master-Slave Parallelization Methods for MOEAs - ICAISC 2013.pdf). The same preliminary draft of the document can be previewed at the bottom of this post. The original publication is available at the IEEE Xplore website.

For citations please use the following BibTeX reference:

@INPROCEEDINGS{Zuavoianu2013SYNASC,
  author = {Alexandru-Ciprian Z\u{a}voianu and Edwin Lughofer and Gerd Bramerdorfer and Wolfgang Amrhein and Erich Peter Klement},
  title = {An Effective Ensemble-Based Method for Creating On-the-Fly Surrogate Fitness Functions for Multi-Objective Evolutionary Algorithms},
  booktitle = {Proceedings of the 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2013)},
  year = {2013},
  pages = {235-248},
  publisher = {IEEE Computer Society},
}

Paper: Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution - EUROCAST 2013

This paper was prepared for the EUROCAST 2013 conference and contains a proof-of-concept for DECMO - a simple coevolutionary multi-objective optimization algorithm that, on several problems is successfully able to adopt the search behavior of the most successful strategy it incorporates. This line of research was continued and fully fleshed out in DECMO2 - a subsequent algorithm I developed for my PhD. Here is the abstract our EUROCAST 2013 submission:

We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.

You can download a preliminary version of the paper by clicking here or from my Downloads box (Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution - EUROCAST 2013.pdf). The same preliminary draft of the document can be previewed at the bottom of this post. The original publication is available at www.springerlink.com.

For citations please use the following BibTeX reference:

@INCOLLECTION{Alexandru-CiprianZavoianu2013,
  author = {Alexandru-Ciprian Z\u{a}voianu and Edwin Lughofer and Wolfgang Amrhein and Erich Peter Klement},
  title = {Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution},
  booktitle = {Computer Aided Systems Theory - EUROCAST 2013},
  publisher = {Springer Berlin / Heidelberg},
  year = {2013},
  editor = {Moreno-Diaz, Roberto and Pichler, Franz and Quesada-Arencibia, Alexis},
  volume = {8111},
  series = {Lecture Notes in Computer Science},
  pages = {251-258},
  doi = {10.1007/978-3-642-53856-8_32},
}