Colony-Based Search Algorithm for numerical optimization

The efficiency of an Evolutionary Algorithm is highly sensitive to the mutation and crossover operators it possesses, as well as to the strategy used for determining the direction of numerical evolution and the values of evolutionary step sizes. There is no analytical method to efficiently define the direction of numerical evolution and the value of evolutionary step size for EAs. The efficiency of EAs' search processes is also influenced by their ability to maintain numerical diversity within the population. This paper introduces the Colony-Based Search Algorithm (CSA). The development of CSA was motivated by the scientific and industrial need for a relatively more efficient EA. CSA possesses relatively more efficient artificial genetic operators and strategies for producing evolutionary direction and step size, and the ability to maintain numerical diversity. CSA generates the Clan Matrix containing the pattern vectors to be evolved in the current iteration by randomly selecting pattern vectors from the Colony Matrix at the beginning of each iteration. This makes it easier for CSA to maintain numerical diversity among pattern vectors for a long time. CSA's mutation method includes three randomly blended components with different properties. The problem-solving performance of CSA is statistically compared with the problem-solving performance of eight popular evolutionary search methods (i.e., SADE, SHADE, LSHADE, COBIDE, JADE, CK, GWO, and SFS) by using benchmark functions of CEC'2017 and CEC'2022. In the experiments, the 3D viewshed analysis was addressed as a real-world problem, employing the CSA. The statistical analyses conducted on the experimental results indicate that CSA performs relatively better than the compared methods to solve numerical problems.