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This paper introduces a new approach of a hybrid meta-heuristics-based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. The authors study the traffic network in large-scale routing problems as a field of application. The meta-heuristic they propose introduces a new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimize a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on randomly generated network instances. Experimental analyses demonstrate the efficiency of the proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing techniques in term of the optimal solution in every generation.

Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach