Stochastic evolution: a fast effective heuristic for some generic layout problems
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Theory of Computing Systems
A new approach to multiobjective A* search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Optimal application mapping on NoC infrastructure using NSGA-II and microGA
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
International Journal of Applied Mathematics and Computer Science
Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods
Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods
Fast and accurate estimation of shortest paths in large graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploring the runtime of an evolutionary algorithm for the multi-objective shortest path problem**
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
A faster algorithm for calculating hypervolume
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Dominance-Based Multiobjective Simulated Annealing
IEEE Transactions on Evolutionary Computation
Multi-objective optimal path selection in electric vehicles
Artificial Life and Robotics
Finding Multi-Objective Shortest Paths Using Memory-Efficient Stochastic Evolution Based Algorithm
ICNC '12 Proceedings of the 2012 Third International Conference on Networking and Computing
Hi-index | 0.00 |
Multi-objective shortest path (MOSP) problem aims to find the shortest path between a pair of source and a destination nodes in a network. This paper presents a stochastic evolution (StocE) algorithm for solving the MOSP problem. The proposed algorithm is a single-solution-based evolutionary algorithm (EA) with an archive for storing several non-dominant solutions. The solution quality of the proposed algorithm is comparable to the established population-based EAs. In StocE, the solution replaces its bad characteristics as the generations evolve. In the proposed algorithm, different sub-paths are the characteristics of the solution. Using the proposed perturb operation, it eliminates the bad sub-paths from generation to generation. The experiments were conducted on huge real road networks. The proposed algorithm is comparable to well-known single-solution and population-based EAs. The single-solution-based EAs are memory efficient, whereas, the population-based EAs are known for their good solution quality. The performance measures were the solution quality, speed and memory consumption, assessed by the hypervolume (HV) metric, total number of evaluations and memory requirements in megabytes. The HV metric of the proposed algorithm is superior to that of the existing single-solution and population-based EAs. The memory requirements of the proposed algorithm is at least half than the EAs delivering similar solution quality. The proposed algorithms also executes more rapidly than the existing single-solution-based algorithms. The experimental results show that the proposed algorithm is suitable for solving MOSP problems in embedded systems.