1. What are the contributions mentioned in the paper "An evolutionary algorithm based pattern search approach for constrained optimization" ?
The purpose of this work is to develop a gradient free constrained global optimization methodology to solve this type of problems.. The use of penalty function method will enable to further improve the current best solution by decreasing the level of constraint violation, which is made using a gradient free local search method.
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![TABLE II Comparison of function evaluations (FE) needed by the EA-PS and three existing earlier approaches [7], [27], [37]. Function evaluations by NSGA-II and local search have been shown separately.](/figures/table-ii-comparison-of-function-evaluations-fe-needed-by-the-225rrizq.webp)
![Table I shows the total number of function evaluations (FE), which is the sum of the number of function evaluations taken by EA and the Hooke-Jeeves method, and the corresponding objective function values (f). We compare the results with the previous hybrid method [27] that uses gradient information. The Table I clearly shows that our best number of function evaluation is better than the previous reported one. However, in terms of median and worst of the number of function evaluations the previous method outperform the EA-PS, which is expected since EA-PS does not use gradient information. But the results are comparable. We can conclude that EA-PS method performs successfully.](/figures/table-i-shows-the-total-number-of-function-evaluations-fe-3n1wvjqh.webp)
