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Yves Matanga

Publications and source records attributed to Yves Matanga.

2 recordsLinked to original sources
arXiv2025

Optimal Traffic Relief Road Design using Bilevel Programming and Greedy Seeded Simulated Annealing: A Case Study of Kinshasa

Context: The city of Kinshasa faces severe traffic congestion, requiring strategic infrastructure capacity enhancements. Although a comprehensive master plan has been proposed, its implementation requires substantial financial investment, which remains constrained in the Democratic Republic of the Congo (DRC), an emerging economy. This research proposes a traffic flow based algorithm to support the development of priority road segments. The objective is to enable more effective prioritisation of road construction projects and facilitate the optimal allocation of limited infrastructure budgets. Methods: The study was conducted by formulating a standard transport network design problem (TNDP) that included estimated origin demand data specific to the city of Kinshasa. Given the high computational nature of the 30 node network design, TNDP relevant metaheuristics (GA, ACO, PSO, SA, TS, Greedy) were used selectively and hybridised to achieve high quality, stable solutions. A greedy search seeded simulated annealing and Tabu search were devised to achieve the design goals. Results: Greedy Simulated Annealing and Greedy Tabu search yielded the best travel time reduction and the most stable solutions compared to other solvers, also improving network edge betweenness centrality by nearly a scale of two and a half. Conclusions: Road priorities were proposed, including junctions connecting the Bandundu and Kongo Central entry point to main attraction centres (Limete Poids Lourd, Gombe, Airport) and additional inner city areas (Ngaliema, Selembao, Lemba, Masina, Kimwenza).

cs.NE↗
arXiv2025

An improved clustering-based multi-swarm PSO using local diversification and topology information

Multi-swarm particle optimisation algorithms are gaining popularity due to their ability to locate multiple optimum points concurrently. In this family of algorithms, clustering-based multi-swarm algorithms are among the most effective techniques that join the closest particles together to form independent niche swarms that exploit potential promising regions. However, most clustering-based multi-swarms are Euclidean distance-based and only inquire about the potential of one peak within a cluster and thus can lose multiple peaks due to poor resolution. In a bid to improve the peak detection ratio, the current study proposes two enhancements. First, a preliminary local search across initial particles is proposed to ensure that each local region is sufficiently scouted prior to particle collaboration. Secondly, an investigative clustering approach that performs concavity analysis is proposed to evaluate the potential for several sub-niches within a single cluster. An improved clustering-based multi-swarm PSO (TImPSO) has resulted from these enhancements and has been tested against three competing algorithms in the same family using the IEEE CEC2013 niching datasets, resulting in an improved peak ratio for almost all the test functions.

cs.NE↗