@article{article_481937, title={AN ENSEMBLE INERTIA WEIGHT CALCULATION STRATEGY IN PARTICLE SWARM OPTIMIZATION ALGORITHM}, journal={Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi}, volume={6}, pages={544–558}, year={2018}, DOI={10.15317/Scitech.2018.151}, author={Aydilek, İbrahim Berkan}, keywords={Atalet ağırlığı,Parçacık sürü optimizasyonu}, abstract={<p> <span style="font-size:12.6px;">The ultimate success of particle swarm optimization depends on the velocity values of </span> </p> <p> <span style="font-size:12.6px;">previous particles. Velocity is multiplied with inertia weight coefficient, and has a significant effect on </span> </p> <p> <span style="font-size:12.6px;">search capability of the particle swarm optimization. When looking at previous studies that are carried </span> </p> <p> <span style="font-size:12.6px;">out to calculate this coefficient, it is seen that inertia weight coefficient has been handled in several ways. </span> </p> <p> <span style="font-size:12.6px;">In this article; a new ensemble inertia weight calculation strategy is proposed that uses other constant, </span> </p> <p> <span style="font-size:12.6px;">random, linear decreasing, global local best, simulated annealing and chaotic inertia weight calculation </span> </p> <p> <span style="font-size:12.6px;">methods. Other methods results are combined and used to make a final output decision in a proper way. </span> </p> <p> <span style="font-size:12.6px;">In experimental tests, 30 common optimization benchmark test problems are used. Proposed ensemble </span> </p> <p> <span style="font-size:12.6px;">strategy is proven by statistical tests and gives successful results in all optimization benchmark test </span> </p> <p> <span style="font-size:12.6px;">problems. </span> </p>}, number={4}, publisher={Konya Teknik Üniversitesi}