Prescreening and Repairing in a Genetic Algorithm for Highway Alignment Optimization

Abstract

The method of handling infeasible solutions in an evolutionary search algorithm [e.g., genetic algorithms (GAs)] is crucial to the effectiveness of the solution search process. This problem arises because solution search steps, techniques, and operators used in GAs (such as reproduction, mutation, and recombination) are normally "blind" to the constraints, and thus GAs can generate solutions that do not satisfy the requirements of the problems. In GA-based highway alignment optimization (HAO), many infeasible solutions, which violate model constraints, are also possibly generated, and evaluation of such solutions is wasteful. This study focuses on ways to avoid wasting computation time on evaluating infeasible solutions generated from the GA-based HAO, and develops a prescreening and repairing (P&R) method for an efficient search of highway alignments.The key idea of the P&R method is to repair (before the very detailed alignment evaluation) any candidate alignments whose violations of design constraints can be fixed with reasonable modifications. However, infeasible alignments whose violations of constraints are too severe to repair are discarded (prescreened) before any detailed evaluation procedure is applied. The proposed P&R method is simple, but significantly improves computation time and solution quality in the GA-based HAO process. Such improvements are demonstrated with a test example for a real road project. Through the example study, it is shown that the model incorporating the P&R method can find a good solution much faster (by approximately 23%) than the model with the conventional . penalty method. In addition, the P&R method allows the model to evaluate about 70% more solutions than that it can evaluate with the penalty method for the same number of generations.

Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions

“…GA is a heuristic search algorithm belonging to a class of algorithms known as evolutionary algorithms (Holland, 1975) that are based loosely on the process of natural evolution. GAs have been applied in various disciplines of civil engineering such as construction engineering (Al‐Bazi and Dawood, 2010; Cheng and Yan, 2009), transportation engineering (Vlahogianni et al, 2007; Lee and Wei, 2010), highway engineering (Kang et al, 2009), design optimization (Adeli and Cheng, 1994a, 1994b; Hung and Adeli, 1994; Adeli and Kumar, 1995a, 1995b; Sarma and Adeli, 2000a, 2000b, 2001, 2002; Kim and Adeli, 2001; Mathakari et al, 2007), structural control (Jiang and Adeli, 2008), and environmental pollution (Martínez‐Ballesteros et al, 2010). Hadi and Wallace (1993) developed a hybrid approach that couples a GA with the TRANSYT‐7F program.…”

A Cell-Based Model for Multi-class Doubly Stochastic Dynamic Traffic Assignment

“…A comprehensive evaluation and comparison of using these techniques for solving the proposed problem can be done in the future. Finally, the proposed model can be applied to transport planning and management by extending it to a bilevel model and solving the resultant model by heuristics such as tabu search (e.g., Fan and Machemehl, 2008; Mohan Rao and Shyju, 2010), genetic algorithms (e.g., Adeli and Kumar, 1995a,b; Sarma and Adeli, 2000a,b, 2001, 2002; Kim and Adeli, 2001; Teklu et al, 2007; Jiang and Adeli, 2008; Cheng and Yen, 2009; Kang et al, 2009; Ng et al, 2009; Zeferino et al, 2009; Lee and Wei, 2010; Al‐Bazi and Dawood, 2010), and ant colony heuristics (e.g., Vitins and Axhausen, 2009).…”

Time-Varying Lane-Based Capacity Reversibility for Traffic Management

“…The upper‐level program () can be substituted by a metaheuristic algorithm. In this article, the GA is adopted due to its evident efficiency and effectiveness in literature (e.g., Lee and Wei, 2010; Adeli and Cheng, 1994a,b; Sarma and Adeli, 2001; Mathakari et al, 2007; Teklu et al, 2007; Ng et al, 2009; Unnikrishnan et al, 2009; Kang et al, 2009). Because the proposed formulation is linear bi‐level, the dual variables of the lower‐level constraints () with respect to the upper‐level objective may be approximated in a similar manner as Lin et al (2008).…”

“…Among various streams of meta‐heuristics, the GA has been widely used, and it is recognized as an effective search procedure for these types of difficult optimization problems. Since 1993, GAs have been used in various civil engineering fields, such as construction engineering (e.g., Al‐Bazi and Dawood, 2010; Cheng and Yan, 2009), transportation engineering (e.g., Vlahogianni et al, 2007; Teklu et al, 2007; Lee and Wei, 2010), highway engineering (e.g., Kang et al, 2009), and design optimization (e.g., Adeli and Cheng, 1994a,b; Hung and Adeli, 1994; Adeli and Kumar, 1995a,b; Sarma and Adeli, 2000a,b, 2001, 2002; Kim and Adeli, 2001; Mathakari et al, 2007; Dridi et al, 2008), structural control (e.g., Jiang and Adeli, 2008), and environmental pollution (e.g., Martínez‐Ballesteros et al, 2010).…”