A Modified Genetic Optimization Algorithm Using Ancestor Path Extrapolation

Optimization techniques have become increasingly popular in building performance simulation and design. The relatively high computational expense of simulating a building for a full year has traditionally been a major obstacle in the widespread use of these techniques. Consequently, most practical implementations employ some variety of meta-heuristics which do not necessarily guarantee an optimal solution but also do not require an impractical number of simulations to run. Still, further gains in convergence speed are needed for automated optimization to become truly practical in building design. This paper introduces a modified genetic optimization algorithm which implements a new creation operator to improve the rate of convergence in building performance optimization. The performance of the new algorithm was compared to that of a traditional genetic algorithm using a test case building model with 26 real valued design variables over several optimization runs. The new algorithm displayed a faster rate of convergence in all tests. The improved convergence rate was independent of the initial population fitness and building location. These results show that genetic algorithms with ancestor path extrapolations can greatly reduce the computational time required in building performance optimization. Introduction The goal of optimization in building simulation is to determine the set of design parameters that optimize one or more objective functions. The essential difficulty in these optimizations is that the evaluation of the most popular objective function (building energy consumption) is very time consuming. This has led to the wide use of metaheuristic optimization algorithms, which do not guarantee an optimal solution but also do not require a large number of calls to the building simulation engine compared to other methods (Nguyen et al. 2014). One of the most popular classes of meta-heuristic algorithms used in building performance optimization is the class of genetic algorithms (GA). These algorithms attempt to optimize building performance by mimicking natural selection over a number of generations of potential solutions. Implementations of the basic GA have been successfully used to solve a variety of building performance optimization problems (Palonen et al. 2009, Rahmani Asl et al. 2014, Xu et al. 2016). The ready availability of the basic GA in packages such as jMetal (Durillo and Nebro 2011) and Optimo (Rahmani Asl et al. 2015) have also helped increase the popularity of this algorithm in building design. Implementations of a genetic optimization algorithm can still be very time consuming, with run times being reported as high as 23 hours to simulate 30 generations (Xu et al. 2016). As a result, additional techniques have been used to reduce this burden. One of these techniques is to reduce the initial population size or the total number of generations; however, this can result in the selection of sub-optimal solutions. Another technique, the use of surrogate models, has been shown to effectively reduce the computational burden associated with running GAs on building models (Magnier and Haghighat 2009, Tresidder et al. 2011, Aijazi and Glicksman 2016). Surrogate models are simplified models which are constructed to have behavior similar to the actual detailed building model. This technique reduces computation time because the surrogate model executes much faster than the detailed model when evaluating the objective function; however, there is some loss in accuracy when using the surrogate model. This paper introduces a method for creating new individuals in a genetic algorithm based on the paths created by ancestors. When used in conjunction with traditional crossover and mutation, this method can reduce the time required to approach an optimal solution. Basic Genetic Algorithm Overview The basic genetic algorithm for single objective optimization can be defined as shown in Figure 1. Figure 1: Basic Genetic Algorithm