A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environments

The solution spaces of many real-world optimization problems change over time. Such problems are called dynamic optimization problems (DOPs), which pose unique challenges that necessitate adaptive strategies from optimization algorithms to maintain optimal performance and responsiveness to environmental changes. Tracking the moving optimum (TMO) is an important class of DOPs where the goal is to identify and deploy the best-found solution in each environments Multi-population dynamic optimization algorithms are particularly effective at solving TMOs due to their flexible structures and potential for adaptability. These algorithms are usually complex methods that are built by assembling multiple components, each of which is responsible for addressing a specific challenge or improving the tracking performance in response to changes. This survey provides an in-depth review of multi-population dynamic optimization algorithms, focusing on describing these algorithms as a set of multiple cooperating components, the synergy between these components, and their collective effectiveness and/or efficiency in addressing the challenges of TMOs. Additionally, this survey reviews benchmarking practices within this domain and outlines promising directions for future research

PSO-Particle Swarm Optimization

Práce se zabývá optimalizací na bázi částicových hejn. V teoretické části je nejprve stručně popsána problematika optimalizace. Poté se značná část věnuje celkovému popisu optimalizačního algoritmu na bázi částicových hejn (PSO). Jsou popsány jeho princip, chování, parametry, struktura a modifikace. Následuje rešerše variant PSO, včetně hybridizací PSO. V praktické části práce jsou nejprve blíže rozebrány dynamické problémy. Poté je popsán nově navržený algoritmus pro dynamické problémy AHPSO (z čeho vychází, čím byl inspirován a jaké prvky používá a proč). Algoritmus je spuštěn na sadě úloh (Moving peaks benchmark) a porovnán s dosud nejlepšími veřejně dostupnými algoritmy variant PSO na dynamické problémy.This work deals with particle swarm optimization. The theoretic part briefly describes the problem of optimization. The considerable part focuses on the overall description of particle swarm optimization (PSO). The principle, behavior, parameters, structure and modifications of PSO are described. The next part of the work is a recherché of variants of PSO, including hybridizations of PSO. In practical part the dynamic problems are analyzed and new designed algorithm for dynamic problems AHPSO is described (what it is based on, what was inspired, what elements are used and why). Algorithm is executed on the set of tasks (Moving peaks benchmark) and compared with the best publicly available variants of algorithm PSO on dynamic problems so far.