A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

Population clustering methods, which consider the position and fitness of the individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method

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