Artificial algae algorithm (AAA) for nonlinear global optimization

Abstract

In this study, a novel bio-inspired metaheuristic optimization algorithm called artificial algae algorithm (AAA) inspired by the living behaviors of microalgae, photosynthetic species, is introduced. The algorithm is based on evolutionary process, adaptation process and the movement of microalgae. The performance of the algorithm has been verified on various benchmark functions and a real-world design optimization problem. The CEC’05 function set was employed as benchmark functions and the test results were compared with the algorithms of Artificial Bee Colony (ABC), Bee Algorithm (BA), Differential Evolution (DE), Ant Colony Optimization for continuous domain (ACOR) and Harmony Search (HSPOP). The pressure vessel design optimization problem, which is one of the widely used optimization problems, was used as a sample real-world design optimization problem to test the algorithm. In order to compare the results on the mentioned problem, the methods including ABC and Standard PSO (SPSO2011) were used. Mean, best, standard deviation values and convergence curves were employed for the analyses of performance. Furthermore, mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE), which are computed as a result of using the errors of algorithms on functions, were used for the general performance comparison. AAA produced successful and balanced results over different dimensions of the benchmark functions. It is a consistent algorithm having balanced search qualifications. Because of the contribution of adaptation and evolutionary process, semi-random selection employed while choosing the source of light in order to avoid local minima, and balancing of helical movement methods each other. Moreover, in tested real-world application AAA produced consistent results and it is a stable algorithm.