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prof. dr hab. inż.
Byrski, Aleksander
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Jednostka AGH
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- KsiążkaOtwarty dostępAgent-Based Metaheuristics in Search and Optimisation(Wydawnictwa AGH, 2013) Byrski, AleksanderIn the domain of computing, an everlasting requirement for developing new metaheuristics for particular problems, coming right from the well-known no free lunch theorem, may be observed. The need for new search and optimisation methods, hybrid ones in particular, paves the way for the development of different metaheuristics, going beyond classical methods (such as population-based ones). Evolutionary multiagent systems (EMAS), which brings together interesting features of agency (such as autonomy) and inspirations coming from population-based techniques, is a good example of such promising methods. However, constructing complex metaheuristics without a detailed description of their structure and behaviour may become pointless, and novel methods, though yielding promising results in particular cases, may be underestimated, because they have not been fully understood and analysed. This dissertation focuses on the issues concerning the justification of using agent-based metaheuristics (in particular EMAS and its variants), preparing of dedicated formal model, conducting an analysis aimed at proving so-called asymptotic guarantee of success and performing experimental analysis of the considered methods. These issues may be treated as the most important and novel aspects of this dissertation. In the beginning of the monograph, a systematic state-of-the-art review is given, then the concepts of EMAS and its modifications are discussed, later the formal model of structure and dynamics of the system using Markov-chains is described. Finally, the outcomes of a broad series of experiments on selected benchmark and real-world problems are discussed. The results presented in this dissertation are useful for practitioners who would to use agent-based metaheuristics and to obtain a deeper insight into the details of their design, experimental and formal features.
- KsiążkaOtwarty dostępSocio-Cognitive Metaheuristic Computing(Wydawnictwa AGH, 2018) Byrski, AleksanderNature-inspired metaheuristics are very popular these days; their creation is usually justified based on the “no free lunch” theorem by Wolpert and MacReady. However, the creation of novel metaheuristics should be realized with care, not only for the sake of creation (cf. Sörensen reports on superficial metaheuristics); in other words, the inspiration should be solid and well-intended (it would be ideal if such methods were formally verified, however this happens very seldom, because of their complexity). In the case of the metaheuristics presented in this monograph, the inspiration comes from the works of Albert Bandura, a renowned Canadian-American psychologist. One of his most important contributions to contemporary science is the theory of social cognitive learning, showing that people do not only learn from their experiences (trial and error) but also by perceiving other people and (fortunately) their trials and errors. This saves a lot of effort, allowing us to utilize the knowledge gathered by perceiving others in order to build humankind’s self-knowledge. This inspiration leads to the proposal of a socio-cognitive metaheuristic paradigm consisting of the introduction or enhancement of the cognitive properties of particular metaheuristics. The most important achievements in this area are socio-cognitive Ant Colony Optimization and socio-cognitive Particle Swarm Optimization. The introduction of cognitive features into such computing algorithms allows us to reach better efficiency in solving selected hard benchmark problems. In this work, the above-mentioned novel algorithms are presented along with selected experimental results. Moreover, the socio-cognitive computing paradigm is defined, and the relationship of the selected metaheuristic algorithm to this paradigm is discussed. This metaphor is also considered as a reference for the selected classic and agent-based metaheuristics. These algorithms are identified by relating them to the literature background, and the possibilities of enhancing them with socio-cognitive features are discussed. Certain examples of further research are also identified. This monograph is meant to introducea novel perspective on the selected metaheuristics, defining the socio-cognitive computing paradigm and providing guidance in this area for readers who are interested in such nature-inspired computing methods.