Related references
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Article
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Xuan Wu et al.
Summary: In this work, we use Surprisingly Popular Algorithm (SPA) as an additional metric to evaluate particle performance in addition to fitness. This allows particles that are not widely known to be selected as learning exemplars. We also propose a Euclidean distance-based adaptive topology where each particle only connects to a limited number of particles with the shortest Euclidean distance. Moreover, we apply the adaptive topology to heterogeneous populations to solve large-scale problems more effectively.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
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Oceanography
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SWARM AND EVOLUTIONARY COMPUTATION
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Zhengyi Chai et al.
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Computer Science, Artificial Intelligence
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Summary: The traveling salesman problem (TSP) is a challenging problem with various proposed solution methods, including the ant colony optimization (ACO) algorithm. However, the performance of ACO is limited by convergence to local optima and computational accuracy. To address these limitations, this study presents a novel ACO algorithm called HSDACO. HSDACO incorporates heterogeneous population automation, smoothing techniques, differential information updating, and evolutionary state estimation and adjustment to improve solution quality and convergence speed for TSP instances.
APPLIED SOFT COMPUTING
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
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Article
Computer Science, Artificial Intelligence
Ilhan Ilhan et al.
Summary: The traveling salesman problem is a popular combinatorial optimization problem that is difficult to solve and has high time complexity. Researchers use it as a standard test bench to evaluate the performance of new algorithms for solving real-world problems. This study proposes a new simulated annealing algorithm called LBSA-CO, which introduces innovations in solution improvement, crossover, and local search. The algorithm utilizes OX1 and ER operators for faster convergence, and parameter tuning is done using the Taguchi method. Experimental results show that the proposed method outperforms state-of-the-art methods on multiple instances.
NEURAL COMPUTING & APPLICATIONS
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Summary: The article introduces the application of adaptive and non-adaptive optimization methods in deep learning, proposes a new non-adaptive method SGD(MD) based on the idea of difference, and experimental results show that in some cases it is even better than popular adaptive optimization methods.
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APPLIED SCIENCES-BASEL
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Summary: Ant Colony Optimization (ACO) is a widely studied bio-inspired algorithm that generates feasible solutions for combinatorial optimization problems. This study proposes an improved ACO-based method, called ACO with Adaptive Visibility (ACOAV), which utilizes a new distance metric and partial solution updates to generate better solutions for the Traveling Salesman Problem (TSP).
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Lian Lian
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AIN SHAMS ENGINEERING JOURNAL
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Gonzalo Lera-Romero et al.
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INFORMS JOURNAL ON COMPUTING
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Marcos Tostado-Veliz et al.
Summary: This paper addresses the efficient Power-Flow solution of ill-conditioned cases, suggesting modifications to the standard structure of the Continuous Newton's method. A new Power-Flow solver is developed based on the introduced solution framework and the results show promising improvements in robustness and efficiency.
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Davoud Sedighizadeh et al.
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Xiaoling Wang et al.
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Bibi Aamirah Shafaa Emambocus et al.
Summary: The Enhanced Swap Sequence based PSO algorithm is proposed by integrating strategies of XPSO into SSPSO to optimize solving TSP problems. The algorithm has been found to provide better solutions with shorter paths compared to SSPSO, but SSPSO converges faster when applied to TSP.
Article
Computer Science, Artificial Intelligence
Nizar Rokbani et al.
Summary: Heuristic computational intelligence techniques are widely used in solving combinatorial optimization problems in large configurations. The collaborative use of bio-inspired heuristics and meta-heuristics like PSO, FA, FPA, and ACO have shown promising results, particularly in addressing TSP problems. The experimental investigations have demonstrated the effectiveness and efficiency of these hybrid approaches in achieving low error rates and balanced performance/time ratios for various TSP test instances.
Review
Computer Science, Information Systems
Sourabh Katoch et al.
Summary: This paper discusses recent advances in genetic algorithms, analyzing selected algorithms of interest in the research community. It helps new and demanding researchers gain a broader understanding of genetic algorithms. The review covers well-known algorithms, genetic operators, research domains, and future research directions in genetic algorithms.
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Chemistry, Analytical
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Review
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C Blum et al.
ACM COMPUTING SURVEYS
(2003)