Ship pipe routing method based on artificial bee colony algorithm

A ship pipe routing method based on an artificial bee colony algorithm, comprising:

• step 1, simplifying expressions of devices and a layout space to build a mathematical model for a pipe routing problem;
• step 2, generating, by using a chaotic elite opposition-based learning strategy, elite initial solutions of the mathematical model for the pipe routing problem;
• wherein each of the elite initial solutions is constructed as Xi=(Xi,1, Xi,2, . . . Xi,j . . . Xi,D), Xi,j represents a jth node of an ith pipe, and D represents a natural number;
• and the step 2 comprises:
• step 2-1, generating a set of basic solutions, and filtering out elite individuals with most outstanding performances from the set of basic solutions as elite solutions;
• step 2-2, performing chaotic opposition-based learning on the elite solutions to generate another set of solutions;
• and
• step 2-3, filtering out elite individuals from the sets of basic solutions and the another set of solutions to generate the elite initial solutions;
• step 3, continuously updating, by using optimally guided employed bees, spirally searching onlooker bees, and scout bees, the elite initial solutions until a convergence condition is satisfied, to obtain a pipe coordinate solution;
• wherein the step 3 comprises:
• step 3-1, in an employed bee phase, guiding a search direction based on a current best solution and a global best solution, and adjusting, based on influence factors of the current best solution and the global best solution, an influence degree of the current best solution and the global best solution on the search direction, thereby updating the elite initial solutions to obtain first updated solutions;
• wherein the step 3-1 comprises:
• step 3-1-1, calculating the current best solution;
• step 3-1-2, calculating the global best solution;
• and
• step 3-1-3, adjusting, based on the influence factors of the current best solution and the global best solution, the influence degree of the current best solution and the global best solution to control the search direction, thereby updating the elite initial solutions to obtain the first updated solutions;
• wherein an expression of each of the first updated solutions is as follows:
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• wherein φ represents a random number within an interval [−1,1];
• λ1 and λ2 represent the influence factors of the current best solution and the global best solution respectively, being 1.5 and 1 in a first half of iterations, and 1 and 1.5 in a second half of iterations;
• and Xpbest and Xgbest represent the current best solution and the global best solution, respectively;
• step 3-2, in an onlooker bee phase, performing, by onlooker bees, spiral exploitation around the global best solution, exchanging, by the onlooker bees, information with other solutions to search from different angles and directions to increase a possibility of jumping out of a local best solution, thereby updating the first updated solutions to obtain second updated solutions;
• step 4, obtaining the pipe coordinate solution, orthogonalizing the pipe coordinate solution to obtain an orthogonalized coordinate solution, and converting the orthogonalized coordinate solution into a node solution as a final solution for the pipe routing problem.