Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

2 Introduction to Meta‐Heuristic and Evolutionary Algorithms

Summary

This chapter presents a brief review of methods for searching the decision space of optimization problems, describes the components of meta‐heuristic and evolutionary algorithms, and illustrates their relation to engineering optimization problems. Other topics covered in this chapter are the coding of meta‐heuristic and evolutionary algorithms, dealing with constraints, the generation of initial or tentative solutions, the iterative selection of solutions, and the performance evaluation of meta‐heuristic and evolutionary algorithms. A general algorithm that encompasses the steps of all meta‐heuristic and evolutionary algorithms is presented.

2.1 Searching the Decision Space for Optimal Solutions

The set of all possible solutions for an optimization problem constitutes the decision space. The goal of solving an optimization problem is finding a solution in the decision space whose value of the objective function is the best among all possible solutions. One of the procedures applicable for finding the optimum in a decision space is sampling or trial‐and‐error search. The methods that apply trial‐and‐error search include (1) sampling grid, (2) random sampling, and (3) targeted sampling.

The goal of a sampling grid is evaluating all possible solutions and choosing the best one. If the problem is discrete, a sampling network evaluates all possible solutions and constraints. The solution that satisfies all ...