Abstract
In many disciplines, the use of evolutionary algorithms to perform optimizations is limited because of the extensive number of objective evaluations required. In fact, in real-world problems, each objective evaluation is frequently obtained by time-expensive numerical calculations. On the other hand, gradient-based algorithms are able to identify optima with a reduced number of objective evaluations, but they have limited exploration capabilities of the search domain and some restrictions when dealing with noncontinuous functions. In this paper, two PSO-based algorithms are compared to evaluate their pros and cons with respect to the effort required to find acceptable solutions. The algorithms implement two different methodologies to solve widely used engineering benchmark problems. Comparison is made both in terms of fixed iterations tests to judge the solution quality reached and fixed threshold to evaluate how quickly each algorithm reaches near-optimal solutions. The results indicate that one PSO algorithm achieves better solutions than the other one in fixed iterations tests, and the latter achieves acceptable results in less-function evaluations with respect to the first PSO in the case of fixed threshold tests.
Highlights
Optimization is an interesting and crucial aspect in design processes, those related to real world issues
The results indicate that one particle swarm optimization [7] (PSO) algorithm achieves better solutions than the other one in fixed iterations tests, and the latter achieves acceptable results in lessfunction evaluations with respect to the first PSO in the case of fixed threshold tests
The possibility of determining an approximation of the optimal solution in an affordable calculation time is, crucial in many disciplines in which optimization is used. Both algorithms are based on the classical PSO approach but implementing different methodologies to improve its performance
Summary
Optimization is an interesting and crucial aspect in design processes, those related to real world issues The reason for this interest in practical optimization problems has to be found in the intensive computational effort frequently needed to evaluate different solutions. The applicability of Nonlinear Programming algorithms is limited to the availability of the first- or second-order derivatives of the real-world problem to solve Both kinds of methods constitute an efficient gradient-based optimization set of algorithms. These algorithms are strongly influenced by the choice of the starting points, the number of local optima, and shape of the peaks that the functions have. In this paper two hybridized PSO algorithms are evaluated in terms of the computational effort required to solve real world engineering problems.
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