Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evoluti...The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.展开更多
In this study, the efficiency of bitumen and asphalt mixtures modified with crumb rubber(CR) and recycled glass powder(RGP) is evaluated. From an environmental point of view, the application of RGP in asphalt mixtures...In this study, the efficiency of bitumen and asphalt mixtures modified with crumb rubber(CR) and recycled glass powder(RGP) is evaluated. From an environmental point of view, the application of RGP in asphalt mixtures is considered beneficial since it prevents accumulation of waste glass in the natural environment. Rheological and mechanical properties of modified bitumen samples were investigated by laboratory methods such as bitumen conventional tests and dynamic shear rheometer(DSR), and also by asphalt mixture performance tests including Marshall stability, indirect tensile strength(ITS),compressive strength, and indirect tensile stiffness modulus. The results showed that the application of RGP in place of CR has no negative impact on the efficiency of bitumen and asphalt mixtures and even improves their engineering properties except for the toughness index(TI). Moreover, a modification with 5% CR and 5% RGP in asphalt mixtures results in the best overall performance.展开更多
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
文摘The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.
文摘In this study, the efficiency of bitumen and asphalt mixtures modified with crumb rubber(CR) and recycled glass powder(RGP) is evaluated. From an environmental point of view, the application of RGP in asphalt mixtures is considered beneficial since it prevents accumulation of waste glass in the natural environment. Rheological and mechanical properties of modified bitumen samples were investigated by laboratory methods such as bitumen conventional tests and dynamic shear rheometer(DSR), and also by asphalt mixture performance tests including Marshall stability, indirect tensile strength(ITS),compressive strength, and indirect tensile stiffness modulus. The results showed that the application of RGP in place of CR has no negative impact on the efficiency of bitumen and asphalt mixtures and even improves their engineering properties except for the toughness index(TI). Moreover, a modification with 5% CR and 5% RGP in asphalt mixtures results in the best overall performance.
