Ikram Twir

About

Ikram Twir is a researcher specializing in combinatorial optimization, metaheuristic algorithms, and their real-world applications in network routing, IoT, robotics, and manufacturing. Twir’s major contributions lie in developing hybrid optimization frameworks that combine swarm intelligence techniques—such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Gravitational Search Algorithm (GSA)—with fuzzy logic and local search mechanisms to solve the Travelling Salesman Problem (TSP), a classic NP-hard benchmark. Their most cited work introduces fuzzy and simplified ant variants supervised by PSO with local search (FAS-PSO-LS, SAS-PSO-LS), achieving 7 citations, while a subsequent hybrid PSOGSA-ACO-LS model, cited 6 and 5 times across two publications, demonstrates enhanced convergence and solution quality. These innovations address critical challenges in path planning and IoT routing, offering efficient, scalable solutions for industrial automation. Twir’s research is notable for its practical impact, bridging theoretical metaheuristics with tangible engineering problems, and their work continues to influence the design of intelligent optimization systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Solving the travelling salesman problem using fuzzy and simplified variants of ant supervised by PSO with local search policy, FAS-PSO-LS, SAS-PSO-LS

   7 citations · 2018
2. 2
   A new hybrid gravitational particle swarm optimisation-ACO with local search mechanism, PSOGSA-ACO-Ls for TSP

   6 citations · 2019
3. 3
   A new hybrid gravitational particle swarm optimisation-ACO with local search mechanism, PSOGSA-ACO-Ls for TSP

   5 citations · 2019

Key Collaborators

Contact & Links