scholarly journals CHAOTIC INITIALIZED MULTIPLE OBJECTIVE DIFFERENTIAL EVOLUTION WITH ADAPTIVE MUTATION STRATEGY (CA-MODE) FOR CONSTRUCTION PROJECT TIME-COST-QUALITY TRADE-OFF

2015 ◽  
Vol 22 (2) ◽  
pp. 210-223 ◽  
Author(s):  
Min-Yuan CHENG ◽  
Duc-Hoc TRAN ◽  
Minh-Tu CAO
Keyword(s):  
Differential Evolution ◽  
Evolutionary Process ◽  
Adaptive Mutation ◽  
Multiple Objective ◽  
Initial Population ◽  
Time Cost ◽  
Nsga Ii ◽  
Trade Off ◽  
Mutation Strategy ◽  
Chaos Sequences

Time, cost and quality are three factors playing an important role in the planning and controlling of construc­tion. Trade-off optimization among them is significant for the improvement of the overall benefits of construction pro­jects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project managers in choosing appropriate plan which is other­wise hard and time-consuming to obtain. The comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previ­ous results verify the efficiency and effectiveness of the proposed algorithm.

Project scheduling with time, cost and risk trade-off using adaptive multiple objective differential evolution

2018 ◽  
Vol 25 (5) ◽  
pp. 623-638 ◽  
Author(s):  
Duc Hoc Tran ◽  
Luong Duc Long
Keyword(s):  
Differential Evolution ◽  
Project Scheduling ◽  
Project Managers ◽  
Adaptive Mutation ◽  
Multiple Objective ◽  
Time Cost ◽  
Trade Off ◽  
Content Type ◽  
Proposed Model ◽  
Mutation Strategy

PurposeAs often in project scheduling, when the project duration is shortened to reduce total cost, the total float is lost resulting in more critical or nearly critical activities. This, in turn, results in reducing the probability of completing the project on time and increases the risk of schedule delays. The objective of project management is to complete the scope of work on time, within budget in a safe fashion of risk to maximize overall project success. The purpose of this paper is to present an effective algorithm, named as adaptive multiple objective differential evolution (DE) for project scheduling with time, cost and risk trade-off (AMODE-TCR).Design/methodology/approachIn this paper, a multi-objective optimization model for project scheduling is developed using DE algorithm. The AMODE modifies a population-based search procedure by using adaptive mutation strategy to prevent the optimization process from becoming a purely random or a purely greedy search. An elite archiving scheme is adopted to store elite solutions and by aptly using members of the archive to direct further search.FindingsA numerical construction project case study demonstrates the ability of AMODE in generating non-dominated solutions to assist project managers to select an appropriate plan to optimize TCR problem, which is an operation that is typically difficult and time-consuming. Comparisons between the AMODE and currently widely used multiple objective algorithms verify the efficiency and effectiveness of the developed algorithm. The proposed model is expected to help project managers and decision makers in successfully completing the project on time and reduced risk by utilizing the available information and resources.Originality/valueThe paper presented a novel model that has three main contributions: First, this paper presents an effective and efficient adaptive multiple objective algorithms named as AMODE for producing optimized schedules considering time, cost and risk simultaneously. Second, the study introduces the effect of total float loss and resource control in order to enhance the schedule flexibility and reduce the risk of project delays. Third, the proposed model is capable of operating automatically without any human intervention.

Hybrid multiple objective evolutionary algorithms for optimising multi-mode time, cost and risk trade-off problem

2019 ◽  
Vol 60 (3) ◽  
pp. 203 ◽  
Author(s):  
Luong Duc Long ◽  
Duc Hoc Tran ◽  
Phong Thanh Nguyen
Keyword(s):  
Evolutionary Algorithms ◽  
Multiple Objective ◽  
Time Cost ◽  
Trade Off ◽  
Multi Mode

An Improved Initial Population Strategy for Compliant Mechanism Designs Using Evolutionary Optimization

2008 ◽  
Author(s):  
Deepak Sharma ◽  
Kalyanmoy Deb ◽  
N. N. Kishore
Keyword(s):  
Local Search ◽  
Optimization Problem ◽  
Optimization Procedure ◽  
Evolutionary Optimization ◽  
Initial Population ◽  
Nsga Ii ◽  
Trade Off ◽  
Continuum Structures ◽  
Population Strategy ◽  
Straight Lines

In this paper, an improved initial random population strategy using a binary (0–1) representation of continuum structures is developed for evolving the topologies of path generating complaint mechanism. It helps the evolutionary optimization procedure to start with the structures which are free from impracticalities such as ‘checker-board’ pattern and disconnected ‘floating’ material. For generating an improved initial population, intermediate points are created randomly and the support, loading and output regions of a structure are connected through these intermediate points by straight lines. Thereafter, a material is assigned to those grids only where these straight lines pass. In the present study, single and two-objective optimization problems are solved using a local search based evolutionary optimization (NSGA-II) procedure. The single objective optimization problem is formulated by minimizing the weight of structure and a two-objective optimization problem deals with the simultaneous minimization of weight and input energy supplied to the structure. In both cases, an optimization problem is subjected to constraints limiting the allowed deviation at each precision point of a prescribed path so that the task of generating a user-defined path is accomplished and limiting the maximum stress to be within the allowable strength of material. Non-dominated solutions obtained after NSGA-II run are further improved by a local search procedure. Motivation behind the two-objective study is to find the trade-off optimal solutions so that diverse non-dominated topologies of complaint mechanism can be evolved in one run of optimization procedure. The obtained results of two-objective optimization study is compared with an usual study in which material in each grid is assigned at random for creating an initial population of continuum structures. Due to the use of improved initial population, the obtained non-dominated solutions outperform that of the usual study. Different shapes and nature of connectivity of the members of support, loading and output regions of the non-dominated solutions are evolved which will allow the designers to understand the topological changes which made the trade-off and will be helpful in choosing a particular solution for practice.

scholarly journals Data Clustering based on Modified Differential Evolution and Quasi-Opposition-based Learning

2020 ◽  
Vol 13 (6) ◽  
pp. 168-178
Author(s):  
Pyae Cho ◽  
◽  
Thi Nyunt ◽  
Keyword(s):  
Differential Evolution ◽  
Heuristic Search ◽  
Clustering Algorithm ◽  
Population Based ◽  
Initial Population ◽  
Local Optima ◽  
Opposition Based Learning ◽  
De Algorithm ◽  
Mutation Strategy

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.

scholarly journals Application of Three Metaheuristic Algorithms to Time-Cost-Quality Trade-Off Project Scheduling Problem for Construction Projects Considering Time Value of Money

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2402
Author(s):  
Omid Kebriyaii ◽  
Ali Heidari ◽  
Mohammad Khalilzadeh ◽  
Jurgita Antucheviciene ◽  
Miroslavas Pavlovskis
Keyword(s):  
Construction Projects ◽  
Metaheuristic Algorithms ◽  
Grey Wolf Optimizer ◽  
Time Cost ◽  
Nsga Ii ◽  
Trade Off ◽  
Time Value Of Money ◽  
Multi Objective ◽  
Time Value ◽  
Value Of Money

Time, cost, and quality have been known as the project iron triangles and substantial factors in construction projects. Several studies have been conducted on time-cost-quality trade-off problems so far, however, none of them has considered the time value of money. In this paper, a multi-objective mathematical programming model is developed for time-cost-quality trade-off scheduling problems in construction projects considering the time value of money, since the time value of money, which is decreased during a long period of time, is a very important matter. Three objective functions of time, cost, and quality are taken into consideration. The cost objective function includes holding cost and negative cash flows. In this model, the net present value (NPV) of negative cash flow is calculated considering the costs of non-renewable (consumable) and renewable resources in each time period of executing activities, which can be mentioned as the other contribution of this study. Then, three metaheuristic algorithms including multi-objective grey wolf optimizer (MOGWO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective particle swarm optimization (MOPSO) are applied, and their performance is evaluated using six metrics introduced in the literature. Finally, a bridge construction project is considered as a real case study. The findings show that considering the time value of money can prevent cost overrun in projects. Additionally, the results indicate that the MOGWO algorithm outperforms the NSGA-II and MOPSO algorithms.

scholarly journals Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Libin Hong ◽  
Chenjian Liu ◽  
Jiadong Cui ◽  
Fuchang Liu
Keyword(s):  
Survival Rate ◽  
Novel Mutation ◽  
Single Point ◽  
Evolutionary Process ◽  
Evolutionary Programming ◽  
Adaptive Mutation ◽  
Single Point Mutation ◽  
Step Size ◽  
Mutation Operators ◽  
Mutation Strategy

Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.

Hybrid multiple objective artificial bee colony with differential evolution for the time–cost–quality tradeoff problem

2015 ◽  
Vol 74 ◽  
pp. 176-186 ◽  
Author(s):  
Duc-Hoc Tran ◽  
Min-Yuan Cheng ◽  
Minh-Tu Cao
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony ◽  
Multiple Objective ◽  
Time Cost ◽  
Bee Colony

scholarly journals Time-Cost Trade-Off Optimization with a New Initial Population Approach

Teknik Dergi ◽  
2019 ◽  
Author(s):  
Vedat TOĞAN ◽  
Mohammad Azim EIRGASH
Keyword(s):  
Initial Population ◽  
Time Cost ◽  
Population Approach ◽  
Trade Off
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