Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting incr...Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.展开更多
Information interaction among particles constitutes a fundamental mechanism in particle swarm optimization( PSO). To address limitations in the efficiency of information interaction and enhance the performance of PSO ...Information interaction among particles constitutes a fundamental mechanism in particle swarm optimization( PSO). To address limitations in the efficiency of information interaction and enhance the performance of PSO in complex optimization landscapes,an elite-sharing and rank-based learning( ESRBL) particle swarm optimization( ESRBL-PSO) framework was proposed in this paper. Departing from the classical PSO framework,where particles primarily interact with the global best information,ESRBL-PSO employs a hierarchical population architecture.Specifically,the original swarm is divided into multiple subpopulations of equal size,each yielding a locally optimal particle( designated as a local elite). These local elites are then aggregated into a shared elite pool,enabling shared information transfer across the entire population. During the updating phase,each particle not only interacts with information within its own subpopulation but also selects a local elite competitively from the shared elite pool for additional information interaction. This dual mechanism amplifies the diversity of information interaction during swarm evolution, facilitating the rapid identification of high-potential regions in expansive search spaces.Furthermore,to mitigate sensitivity to parameters,ESRBL-PSO eliminates all parameters in the particle velocity update process and proposes an adaptive population division strategy. Synergistically,these features enable ESRBLPSO to maintain a balance between exploration diversity and convergence precision,thereby achieving effective optimization across complex problem domains. Finally,extensive experiments executed on CEC2017 benchmark suites verify that ESRBL-PSO exhibits competitive or even superior performance compared to 10 state-of-the-art approaches and maintains robust capability and scalability in handling complex numerical optimization problems.展开更多
基金supported in part by the Ministry of Higher Education Malaysia(MOHE)through Fundamental Research Grant Scheme(FRGS)Ref:FRGS/1/2024/ICT02/UTM/02/10,Vot.No:R.J130000.7828.5F748the Scientific Research Project of Education Department of Hunan Province(Nos.22B1046 and 24A0771).
文摘Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.
文摘Information interaction among particles constitutes a fundamental mechanism in particle swarm optimization( PSO). To address limitations in the efficiency of information interaction and enhance the performance of PSO in complex optimization landscapes,an elite-sharing and rank-based learning( ESRBL) particle swarm optimization( ESRBL-PSO) framework was proposed in this paper. Departing from the classical PSO framework,where particles primarily interact with the global best information,ESRBL-PSO employs a hierarchical population architecture.Specifically,the original swarm is divided into multiple subpopulations of equal size,each yielding a locally optimal particle( designated as a local elite). These local elites are then aggregated into a shared elite pool,enabling shared information transfer across the entire population. During the updating phase,each particle not only interacts with information within its own subpopulation but also selects a local elite competitively from the shared elite pool for additional information interaction. This dual mechanism amplifies the diversity of information interaction during swarm evolution, facilitating the rapid identification of high-potential regions in expansive search spaces.Furthermore,to mitigate sensitivity to parameters,ESRBL-PSO eliminates all parameters in the particle velocity update process and proposes an adaptive population division strategy. Synergistically,these features enable ESRBLPSO to maintain a balance between exploration diversity and convergence precision,thereby achieving effective optimization across complex problem domains. Finally,extensive experiments executed on CEC2017 benchmark suites verify that ESRBL-PSO exhibits competitive or even superior performance compared to 10 state-of-the-art approaches and maintains robust capability and scalability in handling complex numerical optimization problems.
