Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

Preface

Engineers search for designs of new systems that perform optimally and are cost effective or for the optimal operation and rehabilitation of existing systems. It turns out that design and operation usually involve the calibration of models that describe physical systems. The tasks of design, operation, and model calibration can be approached systematically by the application of optimization. Optimization is defined as the selection of the best elements or actions from a set of feasible alternatives. More precisely, optimization consists of finding the set of variables that produces the best values of objective functions in which the feasible domain of the variables is restricted by constraints.

Meta‐heuristic and evolutionary algorithms, many of which are inspired by natural systems, are optimization methods commonly employed to calculate good approximate solutions to optimization problems that are difficult or impossible to solve with other optimization techniques such as linear programming, nonlinear programming, integer programming, and dynamic programming. Meta‐heuristic and evolutionary algorithms are problem‐independent methods of wide applicability that have been proven effective in solving a wide range of real‐world and complex engineering problems. Meta‐heuristic and evolutionary algorithms have become popular methods for solving real‐world and complex engineering optimization problems.

Yet, in spite of meta‐heuristic and evolutionary algorithms’ frequent ...