میلاد جهانگیری

The Interactive Autodidactic School (IAS) optimization algorithm is a simple, effective, and free-parameter metaheuristic algorithm. In this context, the present research work proposes an efficient constraint handling strategy in conjunction with IAS called “Prize-Penalty Strategy (PPS)” for the optimal design of truss structures under stress, deflection, and kinematic stability constraints. Remarkably, PPS not only guides the malfunctioned students as infeasible solutions but it also rewards the talented students as feasible solutions. This fair breeding scheme can provide a motivational and competitive condition to reach the best optimum solution. The performance of the proposed PPS scheme is also compared with four well-known constraint handling strategies (i.e., Static Penalty Function, Dynamic Penalty Function, Superiority of Feasible Solution, and the Epsilon Constraint Strategy). To examine the versatility and competency of the proposed PPS scheme, four different benchmark truss structures are designed by means of IAS in collaboration with the five aforementioned constraint-handling strategies. The obtained convergence history results manifest that the proposed PPS scheme not only gives the best optimal solution but also has consummate performance compared with other constraint-handling strategies. Furthermore, the obtained stability analysis results reveal that the proposed PPS scheme has high reliability in terms of both intensification and diversification.