Adaptive Cone Algorithm

Abstract

This study was conducted to promote a new adaptive cone algorithm (ACA) algorithm. ACA is a metaheuristic technique based on swarm intelligence. ACA contains three steps. Each agent moves closer to the global reference in the first step. Then, each agent searches for a better solution around the current solution in the second step. The global reference searches for better solutions around it in the third step. This algorithm is named cone because the local space size declines linearly during the iterative process. ACA introduces a new adaptability model to improve the exploration strategy when a better solution cannot be achieved. It is conducted by enlarging the local solution space. ACA is challenged to find the final solution for theoretical and practical problems. The 23 functions are chosen as theoretical optimization problems. The portfolio optimization problem is selected as the practical problem. ACA is compared with five algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), marine predator optimization (MPA), average subtraction-based optimizer (ASBO), and pelican optimization algorithm (POA). The result shows that ACA is competitive in finding the optimal solution for 23 functions and outperforms all sparing algorithms in achieving the highest total capital gain in tackling the portfolio optimization problem. ACA is superior to PSO, GWO, MPA, ASBO, and POA in solving 20, 11, 13, 4, and 21 functions, respectively. In the future, ACA can be implemented in solving various practical optimization problems.