TABLE OF CONTENTS - The Professional Green Building Council

Theme B: Creating a livable, healthy and environmentally viable citiesand nonlinear; leading to difficulty to find an optimal solution that minimizes theobjective function elements with a classical optimization method (gradient-basedsearch method). Accordingly, the genetic algorithm (GA) was employed since it isadequate for our optimization problems. For the multi-criteria decision making, the‘A posteriori articulation (search→decide) approach’ was employed using Paretooptimality.2. OPTIMIZATION ALGORITHM2.1 Genetic AlgorithmThe genetic algorithm (GA) solves optimization problems through the evolutionaryprocess. The concept of biological evolution was introduced in [3] and thecomputational algorithm was performed in [4]. It was Schaffer [5] who suggestedthat the GA can be effectively used in solving discontinuous and nonlinearoptimization problems.While the GA is effective in finding the global minima, it is more computationallyextensive than the gradient-based method (Table 1). The GA was employed in thisstudy since it can efficiently solve for nonlinear and nondifferentiable optimizationproblems.Table 1: Comparison of gradient method and GAComparisonGradientGeneticOptimizationAlgorithmNonlinear problem not possible possibleNondifferentiableProblemnot possiblepossiblefinding global minima difficult easycomputing time(speed) fast slow2.2 Pareto OptimalityThe GA optimization is a process of finding global minima from a randomlyselected initial population. The range of feasible solutions is shown in Fig 1 (f1and f2 are objective function elements). The main objective of optimization is not395