Particle Swarm Optimization in Dynamic Environments
Behrouz Minaei2007, Studies in Computational Intelligence
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Abstract
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Particle Swarm Optimization (PSO) is a robust optimization technique, traditionally effective for static problems, but its effectiveness in dynamic environments has gained attention. This research explores the modifications needed for PSO to effectively find and track changing optima, focusing on challenges like diversity loss and outdated memory. Proposed mechanisms for overcoming these challenges include randomization, repulsion, dynamic networks, and multi-populations, supported by empirical results demonstrating improved performance in dynamic optimization scenarios.
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In order to overcome difficult dynamic optimization and environment extrema tracking problems, we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of the search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control (DOC) problems. Measures were made for different dynamic settings and parameters such as, environmental upgrade frequencies, landscape changing speed severity, type of dynamic (linear or circular), and to dramatic changes on the algorithmic search purpose over each test environment (e.g. shifting the extrema). Finally, comparisons were made with traditional Genetic Algorithms (GA) as well as with more recently proposed Co-Evolutionary approaches. SRS, were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able not only to achieve quick adaptive responses, as to maintaining a number of different solutions, while adapting to new unforeseen extrema; the possibility to spontaneously create and maintain different subpopulations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents over dramatic changing periods, and economizing those foraging resources over periods of stabilization. Finally, results prove that the present SRS collective swarm of bio-inspired agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single ant composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work. M Ramos, V., Fernandes, C., Rosa, A.C., Tracking Extrema over Dynamic Environments using Self-Regulatory Swarms.
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In real life, mostly problems are dynamic. Many algorithms have been proposed to handle the static problems, but these algorithms do not handle or poorly handle the dynamic environment problems. Although, many algorithms have been proposed to handle dynamic problems but still, there are some limitations or drawbacks in every algorithm regarding diversity of particles and tracking of already found optima. To overcome these limitations/drawbacks, we have proposed a new efficient algorithm to handle the dynamic environment effectively by tracking and locating multiple optima and by improving the diversity and convergence speed of algorithm. In this algorithm, a new method has been proposed which explore the undiscovered areas of search space to increase the diversity of algorithm. This algorithm also uses a method to effectively handle the overlapped and overcrowded particles. Branke has proposed moving peak benchmark which is commonly used MBP in literature. We also have performed dif...
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