Chaotic coyote algorithm applied to truss optimization problems

The optimization of truss structures is a complex computing problem with many local minima, while metaheuristics are naturally suited to deal with multimodal problems without the need of gradient information. The Coyote Optimization Algorithm (COA) is a population-based nature-inspired metaheuristic of the swarm intelligence field for global optimization that considers the social relations of the coyote proposed to single-objective optimization. Unlike most widespread algorithms, its population is subdivided in packs and the internal social influences are designed. The COA requires a few control hyperparameters including the number of packs, the population size, and the number maximum of generations. In this paper, a modified COA (MCOA) approach based on chaotic sequences generated by Tinkerbell map to scatter and association probabilities tuning and an adaptive procedure of updating parameters related to social condition is proposed. It is then validated by four benchmark problems of structures optimization including planar 52-bar truss, spatial 72-bar truss, 120-bar dome truss and planar 200 bar-truss with discrete design variables and focus in minimization of the structure weight under the required constraints. Simulation results collected in the mentioned problems demonstrate that the proposed MCOA presented competitive solutions when compared with other state-of-the-art metaheuristic algorithms in terms of results quality.