A cooperative-competitive master-slave global-best harmony searchfor ANN optimization and water-quality prediction

作者：Jaddi Najmeh Sadat; Abdullah Salwani^*

来源：Applied Soft Computing, 2017, 51: 209-224.

DOI：10.1016/j.asoc.2016.12.011

• Global-best harmony search
• Artificial neural network
• Cooperative-competitive master-slave
• Multi-population
• Water-quality predictiona

The artificial neural network (ANN) is one of the most accurate and commonly used machine-learning techniques and can learn even complex data by employing metaheuristic algorithms. Harmony search(HS) is a metaheuristic algorithm that imitates the process by which musicians tune their instruments to achieve perfect harmony. Global-best harmony search(GHS) is an effective variant of the HS algorithm that borrows the concept of gbest (globalbest) from particle-swarm optimization (PSO) to improve the performance of HS. Employing a multi-population technique improves the convergence of the algorithm. The master-slave technique is one of the most powerful multi-population techniques. This paper proposes a cooperative-competitive master-slave multi-population GHS (CC-GHS) to train the ANN. To provide the proposed CC-GHS algorithm with strong abilities in both exploration and exploitation, a competitive master-slave strategy (Com-GHS) is interacted with a cooperative master-slave strategy(Coo-GHS). A probabilistic variable is employed to achieve a good balance between cooperativeness and competitiveness. The method is tested on benchmark classification and time-series prediction problems, and statistical analyses demonstrate the ability of the proposed method. The CC-GHS is also applied to are a lworld water-quality prediction problem with promising results.

• 出版日期2017-2