Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

3 Pattern Search

Summary

This chapter explains the pattern search (PS) algorithm. The PS is a meta‐heuristic algorithm that is classified as a direct search method.

3.1 Introduction

Hooke and Jeeves (1961) called the pattern search (PS) a family of numerical optimization methods that do not require calculating the gradient of the objective function in solving optimization problems. Tung (1984) employed the PS algorithm to calibrate the Muskingum model. Neelakantan and Pundarikanthan (1999) calculated an optimal hedging rule for water supply reservoir systems. They applied a neural network model to speed up the optimization process without considering the number of functional evaluations needed in the simulation of the reservoir system operation. Al‐Sumait et al. (2007) presented a new method based on the PS algorithm to solve a well‐known power system economic load dispatch (ELD) problem with valve‐point effect. Bozorg‐Haddad et al. (2013) implemented the PS for groundwater model calibration and compared the performance of the PS with that of the particle swarm optimization (PSO) algorithm. Groundwater models are computer models that simulate and predict aquifer conditions in response to groundwater withdrawal or recharge or some other stress on an aquifer. Mahapatra et al. (2014) proposed a hybrid firefly algorithm and pattern search (h‐FAPS) technique for a static synchronous series compensator (SSSC)‐based power oscillation damping controller design. Khorsandi et al. ...