Hybrid Intelligence Algorithm Process

The hybrid intelligence algorithm process is shown in Fig. 4.2, and the steps are shown in details as below. 

Step 1 Identify and enter the population size pop ize, crossover probability pc, mutation probability pm and termination generations max ge  

Step 2 The population initialization, randomly generated pop size chromosomes. Since population size has a great influence on the results of genetic algorithm, in order to guarantee the diversity of population and to prevent 

Conduct crossover operation and mutation operation of chromosome Execute the stochastic simulation algorithm of Sect. 4.3.2. Use stochastic simulation to test on feasibility of the chromosomes according to the constraints and calculate its objective function value for each valid chromosome  

Calculate the fitness of each chromosome according to its objective value Select chromosomes through a rotating roulette  

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The termination is decided by termination of maximum algebra max gen. If tfitness value and its corresponding individual of the population in the total iterative process are recorded. 

In the hybrid intelligent algorithm, set population size N = 120, crossover probability pc = 0.5, mutation probability p = 0.5, number of iterations Gmax = 15,000, in the rank-based evaluation function a = 0.05 and randomly simulated 3000 times. Figure 4.6 shows the convergence process of the objective function of the example. Table 4.26 shows the objective function values and total profit in the supply chain at each stage. 

In hybrid intelligent algorithm, confidence level j = 0.9, population size N = 90, crossover probability = 0.6, mutation probability = 0.5, iteration times Gmax = 30,000, in a ranking-based evaluation function a = 0.05, and the times of simulation loop are 6000. The frequency of the PC is 2400 MHz and the algorithm process is conducted in C++ programming language. 

In order to further test the validity of the algorithm and the stochastic chance-constrained programming model, we start the simulation process by taking different values of parameters of the hybrid intelligent algorithm. 

The process for the hybrid intelligent algorithm is illustrated in Fig. 6.2, the detailed steps are shown as following ... 

In Hybrid Intelligent Algorithm, population size N = 70, crossover probability Pc = 0.5, mutation probability pm = 0.6, iteration times Gmax = 10,000, number of times for fuzzy stochastic simulation is 6000, with 3000 training samples. Figure 6.10 illustrates the convergence process of the objective function. 

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