Stochastic time-cost optimization using non-dominated archiving ant colony approach

This article employs a non-dominated archiving ant colony approach to solve the stochastic time-cost trade-off optimization problem. The model searches for non-dominated solutions considering total duration and total cost of the project as two objectives. In order to expect more realistic outcomes for the time-cost trade-off problem, uncertainties in time and cost of the project should be taken into account. Fuzzy sets theory is used to answer for uncertainties in time and cost of the project. The model embeds the α-cut approach to account for accepted risk level of the project manager. Left and right dominance ranking method is used for finding non-dominated solutions. The ranking method employs decision maker's optimism using β concept. The performance of the model is tested according to performance metrics for multi-objective evolutionary algorithms proposed in the literature. The results show that the algorithm is adequately reliable. A case study is solved to show the application of the proposed model for the uncertain time-cost trade-off problem.     

Design of tall bridge piers by ant colony optimization

This paper describes a methodology for the analysis and design of Reinforced Concrete (RC) tall bridge piers with hollow rectangular sections, which are typically used in deep valley bridge viaducts. Piers are usually considered tall when the shaft has a height of 50 m or more. Three different types of rectangular hollow tall piers have been studied for road piers of 90.00 m in height: RTRA90, RLON90 and RLT90. RTRA90 has the two side walls inclined, RLON90 has the two frontal walls inclined and RLT90 has all four walls inclined. The procedure used in the present study to solve the combinatorial problem is a variant of the ant colony optimization. RTRA90 leads to the most economical pier, both in column and foundation cost, since it is the most efficient set up for horizontal loads. Regarding the cost of the vertical column only, i.e. excluding the foundation, the cost of RTRA90 and RLON90 are similar, but the cost of the column RLT90 is higher due to its larger unit cost of interior formwork.     

A particle swarm ant colony optimization for truss structures with discrete variables

In this paper, a particle swarm optimizer with passive congregation (PSOPC), ant colony optimization (ACO) and harmony search scheme (HS) are combined to reach to an efficient algorithm, called discrete heuristic particle swarm ant colony optimization (DHPSACO). This method is then employed to optimize truss structures with discrete variables. The DHPSACO applies a PSOPC for global optimization and the ant colony approach for local search, similar to its continuous version. The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints. Some design examples are tested using the new method and their results are compared to those of PSO, PSOPC and HPSO algorithms to demonstrate the effectiveness of the present method.     

Performance-based seismic design of steel frames using ant colony optimization

In this paper, a performance-based optimal seismic design of frame structures is presented using the ant colony optimization (ACO) method. This discrete metaheuristic algorithm leads to a significant improvement in consistency and computational efficiency compared to other evolutionary methods. A nonlinear analysis is utilized to arrive at the structural response at various seismic performance levels, employing a simple computer-based method for push-over analysis which accounts for first-order elastic and second-order geometric stiffness properties. Two examples are presented to illustrate the capabilities of ACO in designing lightweight frames, satisfying multiple performance levels of seismic design constraints for steel moment frame buildings, and a comparison is made with a standard genetic algorithm (GA) implementation to show the superiority of ACO for the discussed optimization problem.     

Optimal transient network rehabilitation using multi-objective ant colony optimization algorithm

ABSTRACT

In this paper, the problem of optimal rehabilitation of water distribution networks (WDNs) for both steady and transient state is investigated. Two objectives are considered in this study: minimizing rehabilitation cost by considering pipe size diameters as decision variables and minimizing the transient impacts by minimizing a surge damage potential factor (SDPF). A multi-objective ant colony optimization (MOACO) model is developed to solve this problem. The developed model is verified using the well-known New York City tunnel network. The application is carried out to modify an existing real WDN in New Cairo district designed in steady-state conditions to cope with the transient condition. The advantage of this study is to produce a set of solutions that help decision makers to identify the suitable approach, while evaluating and aggregating the preferences over multiple objective functions.     

A tri-objective ant colony optimization based model for planning safe construction site layout

Realizing safety improvements in construction site layout planning (CSLP) is vitally important to construction project safety management. Unlike previous studies in which the safety objective is built without detailed risk factors analysis, this study transforms CSLP into a multi-objective optimization (MOO) problem with designing two safety objective functions due to facility safety relationships (potential risks arising from interaction flows) and geographic safety relationship (potential risks arising from hazardous sources) from the holistic interpretation of interaction relationship connecting temporary facilities. Besides, a supplementary cost reduction objective function was also derived as cost is a critical barrier against safety improvement. Subsequently, a tri-objective ant colony optimization based model was developed to solve MOO problem. Finally, a case study is used to verify the proposed model. The study enriches safety implications by considering onsite safety issues from interaction relationship and enhances site safety of CSLP in the pre-construction stage.     

强光下多尺度图像增强和蚁群算法的火焰分割

针对强光下火焰图像分割不完整的问题,提出了彩色火焰图像增强及改进蚁群算法的阈值自适应分割方法.首先对采集设备捕获的RGB图像进行基于带颜色恢复的多尺度Ret-inex增强,用亮度控制因子对背景像素进行亮度提升,并从3个不同尺度对原图动态增加高频信息,提高火焰局部可见性,使火焰的颜色、纹理和边缘信息更加突出;然后,为了避...     

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.     

基于改进蚁群算法在集装箱码头泊位优化中的应用

探讨了蚁群算法在码头泊位优化中的应用问题。以一定时间内所有船舶在港总时间最短作为优化目标,建立泊位优化配置模型。在求解中,利用改进蚁群算法确定了停靠在每个泊位上船舶的作业顺序。然后,在实例场景与最初的先到先服务算法相比,改进的多态蚁群算法在停港时间上有较大的提高。     

The application of the ant colony optimization algorithm to the construction site layout planning problem

A good site layout is vital to ensure the safety of the working environment, and for effective and efficient operations. Moreover, it minimizes travel distance, decreases materials handling, and avoids the obstruction of materials and plant movement. Based on studies in the manufacturing industry, the cost of materials handling could be reduced by 20–60% if an appropriate facility layout is adopted. In designing a site layout, a planner will first position the key facilities that influence the method and sequence of construction, and then assign the remaining facilities in the available space that is left over. This process is similar to the positioning of facilities in the ant colony optimization (ACO) algorithm. The general principle of the ACO algorithm is to assign facilities to a location one by one, and the occupied locations are deleted from the location scope in the next assignment. In the study, ACO algorithm is employed to resolve the construction site layout planning problem in a hypothetical medium‐sized construction project. By applying fuzzy reasoning and the entropy technique, the study calculates the closeness relationship between facilities, in which the optimal site layout is affected by the mutual interaction of facilities.     

多粒度PPI网络描述模型及其蚁群优化的功能模块检测方法

针对蚁群算法在大规模蛋白质相互作用(protein-protein interaction,PPI)网络中进行功能模块检测所暴露的时间性能方面的不足,提出了一种基于多粒度描述和蚁群优化的快速求解算法。首先,从粒度计算的角度,给出了一种新的多粒度PPI网络描述模型;然后,基于该模型,设计了融合功能和结构信息的粒度划分,粗粒度的蚁群寻优,解的还原与优化3个阶段的求解过程。在大规模PPI网络上的实验表明:算法在保证检测质量的同时,能显著降低利用蚁群算法进行功能模块检测的求解时间,而且与近年来的一些经典算法相比在检测精度上也具有一定的优势。     

Improved selection strategy for multi-objective evolutionary algorithms with application to water distribution optimization problems

Multi-objective evolutionary algorithms (MOEAs) have been applied to water distribution system (WDS) optimization problems for over two decades. The selection strategy is a key component of an MOEA that determines the composition of a population, and thereby the evolutionary search process, which imitates natural selection by granting fitter individuals an increasing opportunity to reproduce. This paper proposes the convex hull contribution (CHC) selection strategy for generational MOEAs (CHC_Gen) as a novel selection strategy that is based on the CHC of solutions to the Pareto front in the objective space. Numerical experiments using a general MOEA framework demonstrate that the CHC_Gen selection strategy is able to outperform existing popular selection strategies (e.g., crowding distance, hypervolume contribution, and hybrid replacement selection). Moreover, it is illustrated that the CHC_Gen selection strategy is able to improve the performance of existing MOEAs such as NSGA-II and GALAXY. The conclusions are based on the results of six bi-objective WDS problems.     

蚁群算法在雨水管道系统优化设计中的新尝试

引入一种新的优化设计方法——蚁群算法,并探讨将该法应用于雨水管道系统的优化设计.通过实例说明蚁群算法的过程,并与遗传算法进行比较,表明蚁群算法在给排水管网设计中有较为广阔的应用前景.     

Simulation optimization for earthmoving operations using genetic algorithms

This paper presents a methodology for simulation optimization utilizing genetic algorithms and applies it to a newly developed simulation-based system for estimating the time and cost of earthmoving operations. The genetic algorithm searches for a near-optimum fleet configuration that reduces project total cost, and considers a set of qualitative and quantitative variables that influence earthmoving operations. Qualitative variables represent the models of equipment used in each fleet scenario, whereas quantitative variables represent the number of items of equipment involved in each scenario. Pilot simulation runs were carried out for all configurations generated by the developed algorithm, and a complete simulation analysis was then performed for the fleet recommended by the algorithm. The numerical example demonstrates the use of the proposed methodology and illustrates its essential features.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization

In solving optimization problems for building design and control, the cost function is often evaluated using a detailed building simulation program. These programs contain code features that cause the cost function to be discontinuous. Optimization algorithms that require smoothness can fail on such problems. Evaluating the cost function is often so time-consuming that stochastic optimization algorithms are run using only a few simulations, which decreases the probability of getting close to a minimum. To show how applicable direct search, stochastic, and gradient-based optimization algorithms are for solving such optimization problems, we compare the performance of these algorithms in minimizing cost functions with different smoothness. We also explain what causes the large discontinuities in the cost functions.     

Efficient algorithms for the reliability optimization of tall buildings

Modern tall buildings are often characterized by their slenderness and sensitivity to extreme wind events. For these buildings traditional least weight optimization procedures based on a few idealized equivalent static wind loads derived from directionless wind models may be inadequate. This is especially true considering traditional models used for combining aerodynamics and site specific climatological information. Indeed these methods were developed for buildings with statistically and mechanically uncoupled systems exhibiting strong preferential behavior for certain wind directions. Using these models during a traditional deterministic optimization may lead to unsafe designs. In this paper a recently developed component-wise reliability model is used to rigorously combine the directional building aerodynamics and climatological information. An efficient reliability-based design optimization scheme is then proposed based on decoupling the traditionally nested optimization loop from the reliability analysis. The decoupling is achieved by assuming the level cut sets containing the mean wind speeds generating a response with specified exceedance probability independent of changes in the design variable vector. The decoupled optimization problem is solved by defining a series of approximate explicit sub-problems in terms of the second order response statistics of the constrained functions.     

Probabilistic unit cost estimation for project configuration optimization

A probabilistic approach to unit cost estimation and project configuration optimization has been developed and explained. A worked example included is intended to illustrate the pitfalls of relying excessively on deterministic cost appraisal.     

粒子群-鱼群混合算法在桁架结构优化设计中的应用

群智能算法由于其优异的搜索性能被广泛应用于结构优化设计,人工鱼群算法和粒子群算法都是基于动物群体行为的智能优化随机算法.本文介绍了人工鱼群算法和粒子群算法的基本原理,并提出了粒子群和鱼群杂交混合的一种新方法:粒子群-鱼群混合算法,将粒子群-鱼群混合算法应用到四个桁架结构的重量优化设计,包括平面桁架结构和空间桁架结构,通过比较粒子群-鱼群混合算法、人工鱼群算法、粒子群算法的优化结果,发现改进的粒子群-鱼群混合算法具有收敛精度高、收敛速度快等特点,同时具有较好的稳定性,可用于结构优化设计.     

An optimization method for selecting project risk response strategies

There is wide agreement that the risk response strategy selection is an important issue in project risk management (PRM). Some academic researchers have paid attention to this issue. This paper proposes a novel method for solving the risk response strategy selection problem in PRM. In the method, an optimization model is developed, which integrates three critical elements that are the project cost, project schedule and project quality. By solving the model, the optimal solution could be obtained so that the most desirable risk response strategies to cope with the risk events can be determined. If the optimal solution is not found or project managers are not satisfied with the solution, another pathway can be used to support the managers to get the desirable strategies. The pathway is based on an iterative process which involves making trade-offs between the project budget, time and quality according to objective requirements and managers' judgments. The iterative process comes to an end if the objectives predefined by the managers are reached. A simple example project is also provided to illustrate the practicality and usefulness of the proposed method.