Abstract
Problem statement: A new multi-objective approach, Strength Pareto Evolutionary Algorithm (SPEA), is presented in this paper to solve the shortest path routing problem. The routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: SPEA handles the shortest path routing problem as a true multi-objective optimization problem with competing and noncommensurable objectives. Results: SPEA combines several features of previous multi-objective evolutionary algorithms in a unique manner. SPEA stores nondominated solutions externally in another continuously-updated population and uses a hierarchical clustering algorithm to provide the decision maker with a manageable pareto-optimal set. SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. Conclusion: The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal nondominated solutions.
Highlights
A computer network is an interconnected group of computers with the ability to exchange data
An ideal routing algorithm should strive to find an optimal path for packet transmission within a specified time so as to satisfy the Quality of Service (QoS)
The objective functions related to cost, time, reliability and risk are appropriated for selecting the most satisfactory route in many communication network optimization problems
Summary
A computer network is an interconnected group of computers with the ability to exchange data. The goal of a multi-objective (SPEA): optimization algorithm is guide the search Initialization: routing path is encoded by a string of towards the Pareto-optimal front and maintain population diversity in the set of nondominated solutions (Zitzler and Thiele, 1999; Deb, 2001; 2008). The length of a routing path should not exceed the maximum length n, where n is the number of nodes in External set: It is a set of Pareto-optimal solutions. Step 1: Each solution i∈P* is assigned a real value, Si∈[0, 1), called strength; Si is proportional to the number, ni of current population members that an external solution i dominates: gene is used to represent the node and its value is used to represent the priority of the node for constructing a path among candidates. Step 6: Compute the reduced nondominated set by uniting the representatives of the clusters
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