الگوریتم فرا‌ابتکاری دسته والد-فرزند مبتنی بر حافظه و خوشه‌بندی جهت بهینه‌‌سازی پویا

تاکنون روش های مختلفی برای بهینه سازی ارایه شده است و یکی از معروف ترین روش های بهینه سازی، الگوریتم های هوش جمعی‌ هستند. بسیاری از مسائل بهینه‌ سازی اخیر در دنیای واقعی طبیعت پویا دارند؛ بنابراین، الگوریتم بهینه‌ سازی برای حل مسائل در محیط ‌های پویا مورد نیاز است. الگوریتم دستۀ والدفرزند مبتنی بر حافظه و خوشه ‌بندی (cmpcs)، گونه‌ ای از الگوریتم‌ های هوش جمعی و برگرفته شده از طبیعت است، که در این مقاله ارایه شده است. این روش به رفتار فردی و گروهی وابسته است، در این الگوریتم برای افزایش کارآیی از یک حافظه با خوشه‌بندی و دافعه استفاده شده است. روش cmpcs پیشنهاد شده بر روی محک قله‌های متحرک (mpb) آزمایش شده است. mpb یک محک خوب برای ارزیابی کارایی الگوریتم‌ های بهینه‌ سازی در محیط ‌های پویا است. نتایج تجربی در mpb نشان می‌ دهد که روش پیشنهادی cmpcs کارایی مناسب ‌تری نسبت به روش‌ های دیگر حل مسائل بهینه‌سازی پویا دارد.

Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization

In the real world, we face some complex and important problems that should be optimized, most of the realworld problems are dynamic. Solving dynamic optimization problems are very difficult due to possible changes in the location of the optimal solution. In dynamic environments, we are faced challenges when the environment changes. To respond to these changes in the environment, any change can be considered as the input of a new optimization problem that should be solved from the beginning, which is not suitable because it is time consuming. One technique for improving optimization and learning in dynamic environments is by using information from the past. By using solutions from previous environments, it is often easier to find promising solutions in a new environment. A common way to maintain and exploit information from the past is the use of memory, where solutions are stored periodically and can be retrieved and refined at the time that the environment changes. Memory can help search respond quickly and efficiently to change in a dynamic problem. Given that a memory has a finite size, if one wishes to store new information in the memory, one of the existing entries must be discarded. The mechanism used to decide whether the candidate entry should be included in the memory or not, and if so, which of the old entries should be replaced it, is called the replacement strategy. This paper explores ways to improve memory for optimization and learning in dynamic environments. In this paper, a memory with clustering and new replacement strategy for storing and restoring memory solutions has been used to enhance memory performance. The evolutionary algorithms that have been presented so far have the problem of rebuilding populations when multiple populations converge to an optimum. For this reason, we proposed algorithm with exclution mechanism that have the ability to explore the environment (Exploration) and extraction (Explitation). Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clustering and Memorybased ParentChild Swarm Algorithm (CMPCS), is presented. This method relies on both individual and group behavior. The proposed CMPCS method has been tested on the moving peaks benchmark (MPB). The MPB is a good Benchmark to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMPCS method compared to the other stateoftheart methods in solving the dynamic optimization problems.