scholarly journals Investigation of optimal designs for concrete cantilever retaining walls in different soils

2020 ◽  
Vol 11 (2) ◽  
pp. 39 ◽  
Author(s):  
Esra Uray ◽  
Serdar Çarbaş ◽  
İbrahim Hakkı Erkan ◽  
Murat Olgun
Keyword(s):  
Objective Function ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Retaining Wall ◽  
Carbon Dioxide Emission ◽  
Retaining Walls ◽  
Optimization Method ◽  
Stability Conditions ◽  
Bee Colony ◽  
Cantilever Retaining Walls

In this paper, the investigation of the optimum designs for two types of concrete cantilever retaining walls was performed utilizing the artificial bee colony algorithm. Stability conditions like safety factors sliding, overturning and bearing capacity and some geometric instances due to inherent of the wall were considered as the design constraints. The effect of the existence of the key in wall design on the objective function was probed for changeable properties of foundation and backfill soils. In optimization analysis, wall concrete weight which directly affect parameters such as carbon dioxide emission and the cost was considered as the objective function and analyzes were performed according to different discrete design variables. The optimum concrete cantilever retaining wall designs satisfying constraints of stability conditions and geometric instances were obtained for different soil cases. Optimum designs of concrete cantilever retaining wall with the key were attained in some soil cases which were not found the feasible optimum solution of the concrete cantilever retaining wall. Results illustrate that the artificial bee colony algorithm was a favorable metaheuristic optimization method to gain optimum designs of concrete cantilever retaining wall.

scholarly journals Green Product Module Partition Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

2019 ◽  
Vol 301 ◽  
pp. 00021
Author(s):  
Wei Wei ◽  
Yang Zhan
Keyword(s):  
Life Cycle ◽  
Objective Function ◽  
Correlation Matrix ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Green Product ◽  
Multi Objective ◽  
Bee Colony ◽  
Axiomatic Design Theory ◽  
Module Partition

Modular design is an important design method in the mass customization for manufacturing industry. The purpose of this paper is to meet diverse market demands while reducing the impact of products on the ecological environment. Firstly, aiming at the product life cycle process, this paper summarizes the problems encountered in each stage of the product, and introduces five green product module partition principles. Then, through the component correlation matrix, the resource greenness objective function based on the whole life cycle and the polymerization degree objective function based on the component correlation matrix are established respectively by the axiomatic design theory which makes the product mapping from functional domain to structural domain. Next, an improved artificial bee colony algorithm is proposed. Based on the artificial bee colony algorithm, the algorithm applies congestion strategy and fast nondominated sorting strategy to solve the module partition problem of product platform with multi-objective optimization, and a uniformly distributed pare to solution set is generated. Through above steps, the optimization results of module partition are obtained. Finally, an application example of aircraft tail horizontal stabilizer parts is given, and the advantages of the algorithm are proved by comparing with other algorithms.

scholarly journals An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 289
Author(s):  
Songyi Xiao ◽  
Wenjun Wang ◽  
Hui Wang ◽  
Dekun Tan ◽  
Yun Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Computational Results ◽  
Bee Colony ◽  
Elite Strategy ◽  
Large Jump ◽  
Complex Optimization

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

Artificial Bee Colony Algorithm for Reliability Analysis of Engineering Structures

2010 ◽  
Vol 163-167 ◽  
pp. 3103-3109 ◽  
Author(s):  
Hao Jin Li ◽  
Jun Jie Li ◽  
Fei Kang
Keyword(s):  
Reliability Analysis ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Limit State ◽  
Optimization Method ◽  
Iteration Scheme ◽  
State Function ◽  
Engineering Structures ◽  
Bee Colony ◽  
Structure Reliability

Artificial bee colony algorithm is a noval optimization method which is inspired by bee colony foraging behavior. Its use in the structure reliability field presents not only the advantage of its facility of implementation, but also the capability to obtain the design point and the failure probability with good accuracy. And by this method, the reliability index of nonlinear and complex limit state function which iteration scheme may fail to converge could be obtained with efficiency. It is demonstrated by four examples that the present method is reliable and accurate in reliability analysis of engineering structures.

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