The Impact of an Inverse-Buffalo Variant Optimization Algorithm on Search Space Expansion

Abstract

The availability and advanced design of the fifth-generation network are critical for satisfying the real-time needs of apps and users. The existing condition generates demand, which determines the network’s expansion and utilisation. As a result, the expansion must be adjustable enough to accommodate the application’s required solution space. Existing optimization methods often struggle with scalability and efficiency in dynamic network conditions. The research introduces the Inverse-Buffalo Variant Optimisation Algorithm (I-BVOA), to enhance 5G network resource allocation. The proposed I-BVOA addresses these limitations by introducing a novel herd initialization strategy and movement patterns inspired by buffalo behaviors. The proposed method aims to improve network resource allocation and demand handling more effectively than traditional algorithms. Initially, the primary aim is set as the requirement for network utilization. To accomplish this goal, the buffalo’s motions are split into two halves. The first phase involves determining demand and network consumption, which allows for a more constant and lengthier setup. The buffalo’s location is modified according to the demand reduction factor until they receive the most support. In the second phase, the expansion/callback procedure is chosen by deciding which buffalo best meets the requirements. As the expansion occurs, the buffalo that fits well signals the remainder of the herd to broaden the solution space. On the other hand, during the callback procedure, a new network demand can be requested for migration. The final buffalo in the herd is given a challenge that requires finding the solution space, distinguishing itself from the other buffaloes, and diverging from the traditional algorithm. This variation consists of two concurrent processes: producing a maximum solution through diverse herd behaviours and minimizing convergence caused by the herd’s overall mobility. The demonstration of the performance results shows the proposed I-BVOA’s superior performance in terms of convergence speed and solution quality, making a significant advancement in network optimization in comparison to the existing methods.

Keywords

• 5G networks
• buffalo optimization
• convergence
• herd behavior
• search space