Differential evolution with the adaptive penalty method for structural multi-objective optimization

Sun

IET Intelligent Trans Sys

et al. 2022

On‐board energy storage devices (OESD) and energy‐efficient train timetabling (EETT) are considered two effective ways to improve the usage rate of regenerative braking energy (RBE) of subway trains. EETT is less costly but has lower ceilings, whereas OESD, although expensive, maximizes the reuse of RBE. To make the RBE usage rate of the train group reach the required target, a typical problem is how to use the OESD with the minimum capacity to reduce the cost. With this consideration, this paper proposes an optimization method that combines EETT and OESD. In this method, the joint optimization of timetable and OESD capacity allocation in case OESDs of different capacities can be carried between trains is considered to further improve the optimization. A non‐linear programming model is built to minimize the total OESD capacity of the train group under the constraint of reaching target RBE usage. An efficient heuristic algorithm is designed to simultaneously obtain the optimal timetable and the matching capacity allocation scheme of OESDs. Finally, some simulation cases based on real‐line data are designed to verify the effectiveness of the method.

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Joint optimization combining the capacity of subway on‐board energy storage device and timetable

Sun

IET Intelligent Trans Sys

et al. 2022

Numerical Meth Engineering

“…23 The improved versions of the standard metaheuristic algorithms have also come in sight over time to dispose the algorithmic shortcomings of standard ones, recently. [24][25][26][27] As instances from the current ones; for structural engineering field four modified versions of standard grasshopper optimization algorithm to solve large-scale real-size complex both truss and frame type steel structures are proposed, 28 the differential evolution with adaptive penalty method is used for structural multi-objective optimization, 29 the five different trusses are optimized via craziness based particle swarm optimization (CRPSO), which is an adaptive version of the particle swarm optimization (PSO), 30 and so forth. Furthermore, a developed metaheuristic algorithm may give very successful results in one design field, but may not show enough optimal solution performance in another design field.…”

Section: Introductionmentioning

confidence: 99%

Numerical design optimization of real size complex steel space frame structures using a novel adaptive cuckoo search method

Carbas,

Tunca

2023

In this study, real size complex steel space frame structures are numerically designed to achieve optimal design weight. For this aim, standard cuckoo search algorithm is rectified through a newly proposed adaptive method over two fundamental algorithmic parameters of alien egg probability detection (Pa) and step size control (α) to overcome incompetency of trapping into local optima. Besides, to strengthen exploitation phase of newly proposed algorithm, it is boosted with greedy selection (GS). So, novel algorithm has more promising exploration and exploitation capabilities. An 8‐story, 1024‐member and a 20‐story, 1860‐member real size complex steel space frame structures are selected as design examples. Also, initially to verify the supremacy of novel algorithm, a well‐known welded beam is optimized as a benchmark structural design problem. Afterwards, the steel space frame structures are optimally designed via novel algorithm. Since ready steel section lists are utilized as selection pool for design variables, discrete programming problem is come into existence. The dead, live, snow, and wind design loads acting on frame structures are calculated in direction of ASCE 7‐05 provisions. Furthermore, the structural design constraints are determined from LRFD‐AISC specifications. Eventually, the newly proposed adaptive cuckoo search algorithm boosted with GS presents outstanding algorithmic performance.

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

“…A ideia principal é fazer com que o valor dos coeficientes de penalização sejam distribuídos de tal forma que as restrições mais difíceis de serem atendidas sejam penalizadas mais fortemente. Trabalhos como [6] e [7], nos quais a técnica APM foi aplicada com sucesso no tratamento de restrições de problemas de otimização, principalmente em problemas do mundo real, podem ser encontrados na literatura.…”

Section: Introductionunclassified

Análise do algoritmo IWOA acoplado a uma técnica de penalização adaptativa em problemas de otimização com restrições aplicados a projetos de engenharia

Vargas¹,

Lemonge²,

Wanner³

2022

Resumo. O Whale Optimization Algorithm (WOA) é uma metaheurística bioinspirada no comportamento de caça das baleias que tem se destacado em pesquisas recentes devido, principalmente, a sua rápida convergência. Entretanto, isso pode aumentar a probabilidade de que o algoritmo fique preso a ótimos locais. Recentemente foi proposto o Improved WOA (IWOA), que melhora a capacidade de exploração do espaço de busca evitando convergência prematura. Devido ao IWOA ter sido originalmente proposto para problemas sem restrições, esse trabalho propõe ampliá-lo para resolver problemas com restrições através do acoplamento de uma técnica de penalização adaptativa denominada APM. Experimentos numéricos foram realizados para avaliar o desempenho do algoritmo proposto, o qual foi nomeado IWOA+APM. Oito problemas de otimização com restrições aplicados a projetos de engenharia foram utilizados nesses experimentos. Os resultados foram promissores, mostrando que o IWOA+APM foi competitivo neste conjunto de problemas quando comparado à resultados encontrados na literatura.Palavras-chave. Whale optimization, técnica de penalização adaptativa, otimização com restrições, projeto ótimo de engenharia.