Improved Ant Colony Algorithms for Eliminating Stagnation and Local Optimum Problem — A Survey

Amuthan, A.; Thilak, K. Deepa

doi:10.1109/ictacc.2017.33

Cited by 8 publications

(2 citation statements)

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“…These algorithms have been used for solving complex optimization problems [9]. The meta-heuristic algorithms are classified on the foundation of inputs, searching tactics and the complication of exploration and exploitation capabilities [10]. Developing and selecting an efficient algorithm, that could manage multiple objective-based optimization problems, has become a core challenge.…”

Section: Background and Related Work A Meta-heuristic Optimization Algorithmsmentioning

confidence: 99%

“…Constraint (6c) verifies that all selected links for the computed path must accommodate all number of traffic requests in the given topology. Constraint (6d) as a verification of bandwidth price is added on each selected link within limited bounds of [10,100]. Constraint (6e) recognizes that traffic delay must be present to all links.…”

Section: ) Optimal Resource Provisioning Objective Functionmentioning

confidence: 99%

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An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks

et al. 2019

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Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as standard PSO, Adaptive PSO, Bat and Dolphin algorithm.

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Section: Background and Related Work A Meta-heuristic Optimization Algorithmsmentioning

confidence: 99%

Section: ) Optimal Resource Provisioning Objective Functionmentioning

confidence: 99%