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Descrição

In optimization algorithms, there are some challenges, including lack of optimal solution, slow convergence, lack of scalability, partial search space, and high computational demand. Inspired by the process of gold exploration and exploitation, we propose a new meta-heuristic and stochastic optimization algorithm called collaborative gold mining (CGM). The proposed algorithm has several iterations; in each of these, the center of mass of points with the highest amount of gold is calculated for each miner (agent), with this process continuing until the point with the highest amount of gold or when the optimal solution is found. In an n-dimensional geographic space, the CGM algorithm can locate the best position with the highest amount of gold in the entire search space by collaborating with several gold miners. The proposed CGM algorithm was applied to solve several continuous mathematical functions and several practical problems, namely, the optimal placement of resources, the traveling salesman problem, and bag-of-tasks scheduling. In order to evaluate its efficiency, the CGM results were compared with the outputs of some famous optimization algorithms, such as the genetic algorithm, simulated annealing, particle swarm optimization, and invasive weed optimization. In addition to determining the optimal solutions for all the evaluated problems, the experimental results show that the CGM mechanism has an acceptable performance in terms of optimal solution, convergence, scalability, search space, and computational demand for solving continuous and discrete problems.