A Tabu Search Approach With Dynamical Neighborhood Size for Solving the Maximum Min-Sum Dispersion Problem

Lai, Xiangjing; Fu, Zhang-Hua

doi:10.1109/access.2019.2959315

Cited by 8 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genetic algorithms, with their population-based approach and genetic operations, may have a higher computational complexity as they evaluate and evolve multiple solutions in parallel [ 16 ]. However, tabu search’s local search approach and memory-based mechanisms can efficiently explore the neighborhood and avoid revisiting local optima, making it suitable for handling large search spaces [ 17 ]. The algorithm was first introduced in [ 18 ] and has since been extensively studied and applied in various fields [ 19 , 20 ].…”

Section: Proposed Algorithm and Strategiesmentioning

confidence: 99%

Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection

Shi,

Cheng,

Gao

et al. 2024

Sensors

View full text Add to dashboard Cite

In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching for an optimal LED for each UE. Due to the non-convex and non-continuous nature of this security maximization problem, we propose an LED selection algorithm based on tabu search to avoid getting trapped in local optima and expedite the search process by managing trial vectors from previous iterations. Moreover, we introduce three LED selection strategies with a low computational complexity. The simulation results demonstrate that the proposed algorithm achieves a secrecy performance very close to the global optimal value, with a gap of less than 1%. Additionally, the proposed strategies exhibit a performance gap of 28% compared to the global optimal.

show abstract

Section: Proposed Algorithm and Strategiesmentioning

confidence: 99%

Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection

Shi,

Cheng,

Gao

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The traditional COA degrades the convergence rate and prematurely falls into the local optimal solution. There are some approaches that have been tried to solve this problem, such as Tabu search schemes and Greedy algorithms [35][36][37][38][39]. The Tabu search can find a better solution except slow solution speed and long search time.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Pure Sine Wave Inverter with Robust Intelligent Sliding Mode Maximum Power Point Tracking for Photovoltaic Applications

Chang

2020

Micromachines

View full text Add to dashboard Cite

Photovoltaic (PV) power generation has been extensively used as a result of the limited petrochemical resources and the rise of environmental awareness. Nevertheless, PV arrays have a widespread range of voltage changes in a variety of solar radiation, load, and temperature circumstances, so a maximum power point tracking (MPPT) method must be applied to get maximum power from PV systems. Sliding mode control (SMC) is effectively used in PV power generation due to its robustness, design simplicity, and superior interference suppression. When the PV array is subject to large parameter changes/highly uncertain conditions, the SMC leads to degraded steady-state performance, poor transient tracking speed, and unwanted flutter. Therefore, this paper proposes a robust intelligent sliding mode MPPT-based high-performance pure sine wave inverter for PV applications. The robust SMC is designed through fast sliding regime, which provides fixed time convergence and a non-singularity that allows better response in steady-state and transience. To avoid the flutter caused by system unmodeled dynamics, an enhanced cuckoo optimization algorithm (ECOA) with automatically adjustable step factor and detection probability is used to search control parameters of the robust sliding mode, thus finding global optimal solutions. The coalescence of both robust SMC and ECOA can control the converter to obtain MPPT with faster convergence rate and without untimely trapping at local optimal solutions. Then the pure sine wave inverter with robust intelligent sliding mode MPPT of the PV system delivers a high-quality and stable sinusoidal wave voltage to the load. The efficacy of the proposed method is validated on a MPPT pure sine wave inverter system by using numerical simulations and experiments. The results show that the output of the proposed PV system can improve steady-state performance and transient tracking speed.

show abstract

“…The obtained results show that this approach outperformed the BKS for 80 out of 140 benchmark instances. Other approaches employed to solve this problem have been a VNS [48], a TS and a GRASP [49], or a TS with a dynamical neighborhood size [50].…”

mentioning

confidence: 99%

A Multi-Start Biased-Randomized Algorithm for the Capacitated Dispersion Problem

et al. 2022

View full text Add to dashboard Cite

The capacitated dispersion problem is a variant of the maximum diversity problem in which a set of elements in a network must be determined. These elements might represent, for instance, facilities in a logistics network or transmission devices in a telecommunication network. Usually, it is considered that each element is limited in its servicing capacity. Hence, given a set of possible locations, the capacitated dispersion problem consists of selecting a subset that maximizes the minimum distance between any pair of elements while reaching an aggregated servicing capacity. Since this servicing capacity is a highly usual constraint in real-world problems, the capacitated dispersion problem is often a more realistic approach than is the traditional maximum diversity problem. Given that the capacitated dispersion problem is an NP-hard problem, whenever large-sized instances are considered, we need to use heuristic-based algorithms to obtain high-quality solutions in reasonable computational times. Accordingly, this work proposes a multi-start biased-randomized algorithm to efficiently solve the capacitated dispersion problem. A series of computational experiments is conducted employing small-, medium-, and large-sized instances. Our results are compared with the best-known solutions reported in the literature, some of which have been proven to be optimal. Our proposed approach is proven to be highly competitive, as it achieves either optimal or near-optimal solutions and outperforms the non-optimal best-known solutions in many cases. Finally, a sensitive analysis considering different levels of the minimum aggregate capacity is performed as well to complete our study.

show abstract

A Tabu Search Approach With Dynamical Neighborhood Size for Solving the Maximum Min-Sum Dispersion Problem

Cited by 8 publications

References 36 publications

Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection

Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection

High-Performance Pure Sine Wave Inverter with Robust Intelligent Sliding Mode Maximum Power Point Tracking for Photovoltaic Applications

A Multi-Start Biased-Randomized Algorithm for the Capacitated Dispersion Problem

Contact Info

Product

Resources

About