摘要
The optimization of discrete problems is largely encountered in engineering and information domains. Solving these problems with continuous-variables approach then convert the continuous variables to discrete ones does not guarantee the optimal global solution. Evolutionary Algorithms (EAs) have been applied successfully in combinatorial discrete optimization. Here, the mathematical basics of real-coding Genetic Algorithm are presented in addition to three other Evolutionary Algorithms: Particle Swarm Optimization (PSO), Ant Colony Algorithms (ACOA) and Harmony Search (HS). The EAs are presented in as unifying notations as possible in order to facilitate understanding and comparison. Our combinatorial discrete problem example is the famous benchmark case of New-York Water Supply System WSS network. The mathematical construction in addition to the obtained results of Real-coding GA applied to this case study (authors), are compared with those of the three other algorithms available in literature. The real representation of GA, with its two operators: mutation and crossover, functions significantly faster than binary and other coding and illustrates its potential as a substitute to the traditional optimization methods for water systems design and planning. The real (actual) representation is very effective and provides two near-optimal feasible solutions to the New York tunnels problem. We found that the four EAs are capable to afford hydraulically-feasible solutions with reasonable cost but our real-coding GA takes more evaluations to reach the optimal or near-optimal solutions compared to other EAs namely the HS. HS approach discovers efficiently the research space because of the random generation of solutions in every iteration, and the ability of choosing neighbor values of solution elements “changing the diameter of the pipe to the next greater or smaller commercial diameter” beside keeping good current solutions. Our proposed promising point to improve the performance of GA is by introducing completely new individuals in every generation in GA using a new “immigration” operator beside “mutation” and “crossover”.
The optimization of discrete problems is largely encountered in engineering and information domains. Solving these problems with continuous-variables approach then convert the continuous variables to discrete ones does not guarantee the optimal global solution. Evolutionary Algorithms (EAs) have been applied successfully in combinatorial discrete optimization. Here, the mathematical basics of real-coding Genetic Algorithm are presented in addition to three other Evolutionary Algorithms: Particle Swarm Optimization (PSO), Ant Colony Algorithms (ACOA) and Harmony Search (HS). The EAs are presented in as unifying notations as possible in order to facilitate understanding and comparison. Our combinatorial discrete problem example is the famous benchmark case of New-York Water Supply System WSS network. The mathematical construction in addition to the obtained results of Real-coding GA applied to this case study (authors), are compared with those of the three other algorithms available in literature. The real representation of GA, with its two operators: mutation and crossover, functions significantly faster than binary and other coding and illustrates its potential as a substitute to the traditional optimization methods for water systems design and planning. The real (actual) representation is very effective and provides two near-optimal feasible solutions to the New York tunnels problem. We found that the four EAs are capable to afford hydraulically-feasible solutions with reasonable cost but our real-coding GA takes more evaluations to reach the optimal or near-optimal solutions compared to other EAs namely the HS. HS approach discovers efficiently the research space because of the random generation of solutions in every iteration, and the ability of choosing neighbor values of solution elements “changing the diameter of the pipe to the next greater or smaller commercial diameter” beside keeping good current solutions. Our proposed promising point to improve the performance of GA is by introducing completely new individuals in every generation in GA using a new “immigration” operator beside “mutation” and “crossover”.
