In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve...In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.展开更多
We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platfo...We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platform.Through the self-imaging effect of multimode interference(MMI)coupler,the demultiplexing function for 1310 nm and 1550 nm wavelengths is implemented.After that,three parallel SWG-based slots are inserted into the MMI section so that the effective refractive index of the modes can be engineered and thus the beat length can be adjusted.Importantly,these three SWG slots significantly reduce the length of the device,which is much shorter than the length of traditional MMI-based wavelength demultiplexers.Ultimately,by using the PSO algorithm,the equivalent refractive index and width of the SWG in a certain range are optimized to achieve the best performance of the wavelength demultiplexer.It has been verified that the device footprint is only 2×30.68μm^(2),and 1 dB bandwidths of larger than 120 nm are acquired at 1310 nm and 1550 nm wavelengths.Meanwhile,the transmitted spectrum shows that the insertion loss(IL)values are below 0.47 dB at both wavelengths when the extinction ratio(ER)values are above 12.65 dB.This inverse design approach has been proved to be efficient in increasing bandwidth and reducing device length.展开更多
A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its referenc...A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle.The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence.In order to reduce the probability of trapping into a local optimal value,an extremum mutation was introduced into simplex-PSO and simplex-PSO-t(simplex-PSO with turbulence) was devised.Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t,and the experimental results confirmed the conclusions:(1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality;(2) compared PSO with chaos PSO(CPSO),the best optimum index increases by a factor of 1×102-1×104.展开更多
基金supported in part by National Natural Science Foundation of China(62106230,U23A20340,62376253,62176238)China Postdoctoral Science Foundation(2023M743185)Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications Open Fundation(BDIC-2023-A-007)。
文摘In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.
基金supported by the National Natural Science Foundation of China(No.61505160)the Innovation Capability Support Program of Shaanxi(No.2018KJXX-042)+2 种基金the Natural Science Basic Research Program of Shaanxi(No.2019JM-084)the State Key Laboratory of Transient Optics and Photonics(No.SKLST202108)the Graduate Innovation and Practical Ability Training Project of Xi’an Shiyou University(No.YCS22213190)。
文摘We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platform.Through the self-imaging effect of multimode interference(MMI)coupler,the demultiplexing function for 1310 nm and 1550 nm wavelengths is implemented.After that,three parallel SWG-based slots are inserted into the MMI section so that the effective refractive index of the modes can be engineered and thus the beat length can be adjusted.Importantly,these three SWG slots significantly reduce the length of the device,which is much shorter than the length of traditional MMI-based wavelength demultiplexers.Ultimately,by using the PSO algorithm,the equivalent refractive index and width of the SWG in a certain range are optimized to achieve the best performance of the wavelength demultiplexer.It has been verified that the device footprint is only 2×30.68μm^(2),and 1 dB bandwidths of larger than 120 nm are acquired at 1310 nm and 1550 nm wavelengths.Meanwhile,the transmitted spectrum shows that the insertion loss(IL)values are below 0.47 dB at both wavelengths when the extinction ratio(ER)values are above 12.65 dB.This inverse design approach has been proved to be efficient in increasing bandwidth and reducing device length.
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProject(20070533131) supported by Research Fund for the Doctoral Program of Higher Education of China
文摘A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle.The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence.In order to reduce the probability of trapping into a local optimal value,an extremum mutation was introduced into simplex-PSO and simplex-PSO-t(simplex-PSO with turbulence) was devised.Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t,and the experimental results confirmed the conclusions:(1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality;(2) compared PSO with chaos PSO(CPSO),the best optimum index increases by a factor of 1×102-1×104.
