Amount Of Constraint Violations Research Articles

Sizing optimization of truss structures using flower pollination algorithm

The recently developed flower pollination algorithm is used to minimize the weight of truss structures, including sizing design variables. The new algorithm can efficiently combine local and global searches, inspired by cross-pollination and self-pollination of flowering plants, respectively. Furthermore, it implements an iterative constraint handling strategy where trial designs are accepted or rejected based on the allowed amount of constraint violation that is progressively reduced as the search process approaches the optimum. This strategy aims to obtain always feasible optimized designs. The new algorithm is tested using three classical sizing optimization problems of 2D and 3D truss structures. Optimization results show that the proposed method is competitive with other state-of-the-art metaheuristic algorithms presented in the literature.

A Constrained Global Optimization Method Based on Multi-Objective Particle Swarm Optimization

This paper proposes a constrained global optimization method based on Multi-Objective Particle Swarm Optimization (MOPSO). A constrained optimization problem is transformed into another bi-objective problem which minimizes both the original objective function and the total amount of constraint violations. Then, the global optimum of the former problem is obtained as the Pareto optimal solution of the latter one having no constraint violation. In order to find the particular Pareto optimal solution, the proposed method introduces to MOPSO the following operations such as (a) restricting the number of Pareto optimal solutions obtained at each iteration of MOPSO to urge particles to approach the feasible set of the original constrained problem, (b) choosing the most promising Pareto optimal solution as the global best solution so as to exclude solutions dominated by it, and (c) encouraging to add Pareto optimal solutions if the number of them is too small to recover the diversity of search. Numerical examples verify the effectiveness, efficiency and wide applicability of the proposed method. For some famous engineering design problems, in particular, it can find solutions which are comparative to or better than the previously known best ones.