Success history based adaptive multi-objective differential evolution variants with an interval scheme for solving simultaneous topology, shape and sizing truss reliability optimisation

Anosri, Siwakorn; Panagant, Natee; Bureerat, Sujin; Pholdee, Nantiwat

doi:10.1016/j.knosys.2022.109533

Cited by 13 publications

(6 citation statements)

References 56 publications

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“…The spiral factor in the Whale Optimization Algorithm (WOA) has been shown in numerous studies to enhance the search diversity of the SHAMODE algorithm, where the WOA's spiral motion is integrated into the SHAMODE algorithm [29] [30]. During the mutation process, each mutation carrier 𝑢 𝑖,𝐺 has a chance to be further updated with the WOA's spiral motion, which is then activated during crossover, as described in equations ( 8) and ( 9) [31].…”

Section: F Improved Adaptive Spiral Strategy Of Whale Optimization Al...mentioning

confidence: 99%

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

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In the process of daily product development and design, optimizing the shape and external dimensions of the vehicle chassis truss structure while considering both weight and reliability indicators often involves interdisciplinary collaboration and inefficient communication, leading to repetitive mechanical labor and low efficiency. This is widely regarded as a difficult and challenging task. In order to improve the design quality of the chassis truss structure and enhance work efficiency, this paper proposes a novel lightweight multi-objective optimization design system for vehicle chassis trusses based on the ANFIS-SHAMODE-IWOA model. Firstly, an adaptive spiral search strategy is introduced based on the multi-objective hybrid metaheuristic algorithm (SHAMODE) which is based on successful history. Then, a new SHAMODE-IWOA algorithm is proposed. In order to estimate the reliability level of the chassis truss structure under different design parameter combinations, a new ANFIS-SHAMODE-IWOA model is constructed by using the proposed SHAMODE-IWOA algorithm to learn the ANFIS model. Finally, in order to obtain the optimal design parameter combination, multi-objective optimization based on minimum design quality and optimal reliability measurement functions is studied using the SHAMODE-IWOA algorithm. Experimental results show that SHAMODE-IWOA has leading global optimization capability on CEC2017 test set benchmark functions. Compared with other intelligent models, the ANFIS-SHAMODE-IWOA model has better performance in reliability coefficient estimation. At the same time, the SHAMODE-IWOA algorithm obtains a more optimal design parameter combination for the chassis truss, with improvements of 9.5%, 12.5%, and 15% in Mass, Bending, and Torsion indicators, respectively. Finally, the proposed ANFIS-SHAMODE-IWOA multi-objective optimization design system, as a novel intelligent model, can be used to evaluate the reliability of chassis truss structures, improve development and design efficiency, and obtain the best design parameter combination, which is beneficial to improving the level of green intelligent manufacturing design.

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Section: F Improved Adaptive Spiral Strategy Of Whale Optimization Al...mentioning

confidence: 99%

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

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“…The material characteristics for all bars are: Young modulus 206842.719 MPa and density of 7.4 g/cm 3 . There is only one point load applied in node 9 which is 444.82 kN, in the -y direction.…”

Section: Test Examplementioning

confidence: 99%

“…The bulk of research in the truss optimization field is generally based on applying novel optimization algorithms to solve standardized problems [2][3][4][5][6][7]. This type of research provides valuable data on parameter settings for truss problems and shows improvements in convergence and resulting weights.…”

Section: Introductionmentioning

confidence: 99%

Comparing Truss Sizing and Shape Optimization Effects for 17 Bar Truss Problem

Petrović¹,

Kostić²,

Marjanović³

et al. 2022

adeletters

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This article aims to demonstrate the difference in results for minimal weight optimization for a 17 bar truss sizing and shape optimization problem. In order to attain results which can be produced in practice Euler bucking, minimal element length, maximal stress and maximal displacement constraints were used. Using the same initial setup, optimization was conducted using particle swarm optimization algorithm and compared to genetic algorithm. Optimal results for both algorithms are compared to initial values which are analytically calculated. The individual element lengths are observed, along with the overall weight, surface area and included number of different cross-sections.

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“…The MFO algorithm is utilized for wind farm layout optimization in [18]. Anosri et al [19] compared different classes of multi-objective optimization algorithms in the context of reliability design optimization. Among the multi-objective optimization algorithms, one representative is the non-dominated sorting genetic algorithm II (NSGA-II).…”

Section: Introductionmentioning

confidence: 99%

Dual-Stage Optimization Scheduling Model for a Grid-Connected Renewable Energy System with Hybrid Energy Storage

Lu,

Peng,

Sun

2024

Energies

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To operate the grid-connected renewable energy system economically, this study presents a dual-stage optimization scheduling model for grid-connected systems with hybrid energy storage, including day-ahead and intra-days stages. In the day-ahead stage, an economically optimal scheduling model is developed, considering the price peak-to-valley difference. This model aims to enhance the economic efficiency of the system by utilizing hybrid energy storage. In the intra-day stage, more accurate renewable energy forecasts with a shorter time scale are considered. The objectives are to minimize the curtailment rate of renewable energy and to track the day-ahead scheduling outcomes. The NSGA-II algorithm is employed for multi-objective optimization, achieving equilibrium solutions considering multiple optimization objectives. Compared to other published works, the proposed model achieves a balance between different optimization objectives, enabling the system to operate economically and stably. It provides a comprehensive approach to optimize the scheduling of grid-connected systems with hybrid energy storage by considering both economic and operational aspects. Overall, this proposed dual-stage optimization model presents a viable approach to improve economic efficiency and mitigate renewable energy curtailment in grid-connected systems. By effectively integrating renewable energy sources and optimizing their utilization, this model contributes to enhancing the sustainability and optimal operation of the power grid.

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Success history based adaptive multi-objective differential evolution variants with an interval scheme for solving simultaneous topology, shape and sizing truss reliability optimisation

Cited by 13 publications

References 56 publications

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Comparing Truss Sizing and Shape Optimization Effects for 17 Bar Truss Problem

Dual-Stage Optimization Scheduling Model for a Grid-Connected Renewable Energy System with Hybrid Energy Storage

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