This thesis deals with the analysis and application of evolutionary algorithms for optimization problems with multiple objectives. Many application problems involve (i) a system model that is not given in closed analytical form and (ii) multiple, often conflicting optimization criteria. Both traits hamper the appli¬ cation of classical optimization techniques, which require a certain structure ...

In the research of multi-objective optimization algorithm, evolutionary algorithms have considered to be very successful tools. Artificial Immune System (AIS)-based algorithms as one of the viable alternative have also be widely developed in this domain. Over the years, researchers of evolutionary algorithms have extended their interest to many-objective situations; however works in AIS-based a...

The problem of dynamic adaptation prediction algorithms in chaotic environments based on identification the situations-analogs database retrospective observations is considered. Under conditions symmetrical and unsymmetrical dynamics, traditional computational schemes precedent turn out to be ineffective. In this regard, a analysis method evolutionary modeling proposed. Implementation scheme fo...

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for optimization problems (MOPs). However, these progressively improved MOEAs not necessarily been equipped with scalable and learnable problem-solving strategies new grand challenges brought by the scaling-up MOPs continuously increasing complexity from diverse aspects, mainly including expensive...