A solution approach for many challenging and non-differentiable optimization tasks in industries is the use of non-deterministic meta-heuristic methods. Some of these approaches include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). However, with the implementation usage of these robust and stochastic optimization approaches, there are still some predominant issues such as the problem of the potential solution being trapped in a local minima solution space. Other challenges include the untimely convergence and the slow rate of arriving at optimal solutions. In this research study, a tripartite version (PSO-GA-SA) is proposed to address these deficiencies. This algorithm is designed with the full exploration of all the capabilities of PSO, GA and SA functioning simultaneously with a high level of intelligent system techniques to exploit and exchange relevant population traits in real time without compromising the computational time. The design algorithm further incorporates a variable velocity component that introduces random intelligence depending on the fitness performance from one generation to the other. The robust design is validated with known mathematical test function models. There are substantial performance improvements when the novel PSO-GA-SA approach is subjected to three test functions used as case studies. The results obtained indicate that the new approach performs better than the individual methods from the fitness function deviation point of view and in terms of the total simulation time whilst operating with both a reduced number of generations and populations. Moreover, the new novel approach offers more beneficial trade-off between exploration and exploitation of PSO, GA and SA. This novel design is implemented using an object oriented programming approach and it is expected to be compatible with a variety of practical problems with specified input-output pairs coupled with constraints and limitations on the available resources
