PROJECT TITLE :

Leveraged Neighborhood Restructuring in Cultural Algorithms for Solving Real-World Numerical Optimization Problems

ABSTRACT:

Several researchers have developed population-based techniques to unravel numerical optimization problems. Nearly none of these techniques demonstrate consistent performance over a wide selection of issues as these problems differ substantially in their characteristics. Within the state-of-the-art cultural algorithms (CAs), drawback solving is facilitated by the exchange of data between a network of active information sources in the idea house and networks of individuals within the population area. To enhance the performance of CAs, we tend to restructure the social fabric interconnections to facilitate versatile Communication among problem solvers in the population house. Many social network reconfiguration mechanisms and types of Communications are examined. This extended CA is compared with other variants of CAs and other well-known state-of-the-art algorithms on a collection of challenging real-world issues. The numerical results show that the injection of neighborhoods with versatile subnetworks enhances performance on a diverse landscape of numerical optimization problems.