Article “Optimal Design of Distribution Overhead Powerlines using Genetic Algorithms” was accepted for publication in IEEE Transactions on Power Delivery

Article “Optimal Design of Distribution Overhead Powerlines using Genetic Algorithms” was accepted for publication in IEEE Transactions on Power Delivery

Articles
Article "Optimal Design of Distribution Overhead Powerlines using Genetic Algorithms" was accepted for publication in IEEE Transactions on Power Delivery. Congratulations, Graeme! Abstract: As the design of distribution overhead power lines becomes increasingly standardized in the 21st century, the reduced search space of its design parameters allows for increasing opportunities to automate the design process. It involves specifying the distribution utility pole heights and classes, pole-top attachments, and conductor span tensions. A successful algorithm must be able of generating designs that are compliant with applicable codes and utility standards, achieve construction labour and material costs that are commensurate to that of a human-created design, and require a reasonable computation time. A design automation algorithm is created for ATCO Electricity, Alberta, Canada. Based on provided requirements and constraints, it uses the…
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Article “Dimension-wise Particle Swarm Optimization: Evaluation and Comparative Analysis” was published in MDPI Applied Sciences

Article “Dimension-wise Particle Swarm Optimization: Evaluation and Comparative Analysis” was published in MDPI Applied Sciences

Articles
Article "Dimension-wise Particle Swarm Optimization: Evaluation and Comparative Analysis" was published in MDPI Applied Sciences. Congratulations, Justin! Abstract: his article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm optimization semi-independently and compares it with the traditional particle swarm optimization. In addition, the comparison is extended to differential evolution and genetic algorithm. This presented comparative study provides a clear exposition of the effects introduced by the proposed algorithm. Performance of all evaluated optimizers is evaluated based on how well they perform in finding the global minima of 24 multi-dimensional benchmark functions, each having 7, 14, or 21 dimensions. Each algorithm is put through a session of self-tuning with 100 iterations to ensure convergence of their respective optimization parameters. The results confirm that the new variant is a…
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