Multi-objective Genetic Algorithm Approach Research Articles

Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches

This paper deals with a problem of partial flexible job shop with the objective of minimising makespan and minimising total operation costs. This problem is a kind of flexible job shop problem that is known to be NP-hard. Hence four multi-objective, Pareto-based, meta-heuristic optimisation methods, namely non-dominated sorting genetic algorithm (NSGA-II), non-dominated ranked genetic algorithm (NRGA), multi-objective genetic algorithm (MOGA) and Pareto archive evolutionary strategy (PAES) are proposed to solve the problem with the aim of finding approximations of optimal Pareto front. A new solution representation is introduced with the aim of solving the addressed problem. For the purpose of performance evaluation of our proposed algorithms, we generate some instances and use some benchmarks which have been applied in the literature. Also a comprehensive computational and statistical analysis is conducted in order to analyse the performance of the applied algorithms in five metrics including non-dominated solution, diversification, mean ideal distance, quality metric and data envelopment analysis are presented. Data envelopment analysis is a well-known method for efficiently evaluating the effectiveness of multi-criteria decision making. In this study we proposed this method of assessment of the non-dominated solutions. The results indicate that in general NRGA and PAES have had a better performance in comparison with the other two algorithms.

Optimal design and development of a 2-DOF PKM-based machine tool

This paper presents multiobjective optimization of a typical 2-degree-of-freedom (DOF) parallel kinematic machine (PKM) tool that has only single DOF joints. Nondimensional indices, namely global stiffness index (GSI), global conditioning index (GCI), and workspace index, are considered as the objectives for optimization. The indices GSI and GCI depict the variation of stiffness and dexterity of PKM within the workspace. The leg length and distance between two rails on which actuators slide are treated as design variables as these greatly influence the characteristics of PKM. A multiobjective genetic algorithm (MOGA) approach is implemented in MATLAB to find an efficient solution to this complex optimization problem. Fitness function includes inverse kinematics equations, Jacobian and stiffness matrices to compute and optimize the nondimensional indices. First, the optimal value of each index is obtained by single-objective GA. To further improve the results, a hybrid function PATTERNSEARCH is used. This helps to select appropriate boundary conditions for MOGA. To obtain the optimal values of all the three indices, a multiobjective GA is carried out. The results are compared with a conventional exhaustive search method of optimization. The obtained results show that the use of MOGA enhances the quality of the optimization outcome. Secondly, a prototype has been designed and developed with the optimal dimensions. The actual workspace of the PKM and influence of leg collision on the workspace are studied. Finally, a preliminary experimentation was carried out. A comparison between PKM and the three-axis serial kinematic machine tool is presented.

Surface Roughness Optimization in End Milling Using the Multi Objective Genetic Algorithm Approach

In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

Multi-objective genetic optimization of linear construction projects

In the real world, the majority cases of optimization problems, met by engineers, are composed of several conflicting objectives. This paper presents an approach for a multi-objective optimization model for scheduling linear construction projects. Linear construction projects have many identical units wherein activities repeat from one unit to another. Highway, pipeline, and tunnels are good examples that exhibit repetitive characteristics. These projects represent a large portion of the construction industry. The present model enables construction planners to generate optimal/near-optimal construction plans that minimize project duration, total work interruptions, and total number of crews. Each of these plans identifies, from a set of feasible alternatives, optimal crew synchronization for each activity and activity interruptions at each unit. This model satisfies the following aspects: (1) it is based on the line of balance technique; (2) it considers non-serial typical activities networks with finish–start relationship and both lag or overlap time between activities is allowed; (3) it utilizes a multi-objective genetic algorithms approach; (4) it is developed as a spreadsheet template that is easy to use. Details of the model with visual charts are presented. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the scheduling of linear construction projects.

A multi-objective genetic algorithm approach for solving feature addition problem in feature fatigue analysis

Feature fatigue (FF) is used to represent the phenomenon of customer's inconsistent satisfaction with products: customers prefer to choose products with more features and capabilities initially, but after having worked with a product, they become frustrated or dissatisfied with the usability problems caused by too many features. To "defeat" FF, it is essential for designers to decide what features should be added when developing a product to make the product attractive enough and not too hard to use at the same time. In this paper, a feature fatigue multi-objective genetic algorithm (FFMOGA) method is reported for solving the feature addition problem. In the proposed method, fitness functions are established based on Bayesian networks, which can represent the uncertain customer preferences and reflect the relationships among features. The computational experiments on a smart phone case show that the FFMOGA approach can find multiple solutions along the Pareto-optimal frontier for designers to select from, and these obtained solutions have good performance in convergence.

Mixed-model assembly line balancing in the make-to-order and stochastic environment using multi-objective evolutionary algorithms

The present study introduces a multi-objective genetic algorithm (MOGA) to solve a mixed-model assembly line problem (MMALBP), considering cycle time (CT) and the number of stations simultaneously. A mixed-model assembly line is one capable of producing different types of products to respond to different market demands, while minimizing on capital costs of designing multiple assembly lines. In this research, according to the stochastic environment of production systems, a mixed-model assembly line has been put forth in a make-to-order (MTO) environment. Furthermore, a MOGA approach is presented to solve the corresponding balancing problem and the decision maker is provided with the subsequent answers to pick one based on the specific situation. Finally, a comparison is carried out between six multi-objective evolutionary algorithms (MOEA) so as to determine the best method to solve this specific problem.

Sequential coupled process-performance simulation and multi-objective optimization of thin-walled tubes

A sequential coupled nonlinear finite element analysis framework is developed to investigate the effects of sheet-forming process and product design parameters on energy absorption characteristics of thin-walled, multicorner tubes made of a magnesium alloy. Coupling stems from the use of analysis results from one simulation to form the initial state in the subsequent simulation. High fidelity coupled process-performance simulations are used in design of computer experiments for developing analytical surrogate models of process and performance responses as functions of product geometry and process parameters. Rupture, thinning and springback are treated as the manufacturing responses whereas mean crush force, maximum crush force, and mass are used as structural performance measures. An integrated process-performance optimization problem is setup and solved using the multi-objective genetic algorithm approach. The results of coupled simulations show the importance of manufacturing effects on the crush response while the Pareto optimal set highlights the tradeoff among process and performance attributes.

Data Quality Measurement on Categorical Data Using Genetic Algorithm

Data quality on categorical attribute is a difficult problem that has not receivedas much attention as numerical counterpart. Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining.Association rule mining problems can be considered as a multiobjective problem rather than as a single objective one.The main area of concentration was the rules generated by association rule mining using gene tic algorithm. The advantage of using geneticalgorithm is to discover high level predictionrules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithm often used in data mining. Genetic algorithm based approach utilizes the linkage betweenassociation rule and feature selection. In this paper, we put forward a Multi objective genetic algorithm approach for data quality on categorical attributes. The result shows that our approach is outperformed by the objecti ves like accuracy, completeness, comprehensibilityand interestingness.

A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.

Parabolic-trough solar thermal power plant simulation scheme, multi-objective genetic algorithm calibration and validation

Abstract The dynamic simulator design and development of a direct steam generation parabolic-trough solar thermal power plant is detailed in this paper. The dynamic simulator is not only the equation-based object-oriented model but also includes features to facilitate the simulation process. A whole simulator scheme has been developed for that purpose. This simulator scheme considers the issues of fetching and converting sensors data to model inputs and obtaining suitable initial values for the boundary condition problem in the numerical integration. The calibration and validation processes have been tackled, using Matlab as the primary tool. However, several tools were studied and tested. A multi-objective genetic algorithm approach has been chosen for calibrating the dynamic model.

Using genetic algorithms to solve industrial time-cost trade-off problems

Time-cost trade-off analysis is one of the most important aspects of industrial project planning and control. There are trade-offs between time and cost to complete the activities of a project; in general, the less expensive the resources used, the longer it takes to complete an activity. Existing methods for time-cost trade-off problems focus on using heuristics or mathematical programming. These methods, however, are not efficient enough to solve large scale CPM problems. This paper presents a Multi-Objective Genetic Algorithm (MOGA) approach to time-cost trade-off problems (TCTP). Finding optimal decisions is difficult and time-consuming considering the numbers of permutations involved. This type of problem is NP-hard, hence attainment of IP/LP solutions, or solutions via Total Enumeration (TE) is computationally prohibitive. The MOGA approach searches for locally Pareto-optimal or locally non-dominated frontier where simultaneously optimization of time-cost is desired. The application of the proposed algorithm is demonstrated through an example project a real life case. The results illustrate the promising performance of the proposed algorithm.

Incorporation of Variable-Speed Pumping in Multiobjective Genetic Algorithm Optimization of the Design of Water Transmission Systems

Global warming caused by human activities presents serious global risks. Individuals, governments, and industries need to be more energy efficient and contribute to the mitigation of global warming by reducing their greenhouse gas (GHG) emissions. In previous research, GHG emission reduction has been identified as one important criterion in improving the sustainability of urban infrastructure and urban water systems. Within the water industry, opportunities exist for reducing GHG emissions by improving pumping efficiency via the use of variable-speed pumps (VSPs). Previously, VSPs have been used in the optimization of the operation of existing water distribution systems (WDSs). However, in WDS design optimization problems, fixed-speed pumps (FSPs) are commonly used. In this study, a pump power estimation method, developed using a false position method based optimization approach, is proposed to incorporate VSPs in the conceptual design or planning of water transmission systems (WTSs), using optimization. This pump power estimation method is implemented within the solution evaluation process via a multiobjective genetic algorithm approach. A case study is used to demonstrate the application of the pump power estimation method in estimating pump power and associated energy consumption of VSPs and FSPs in WTS optimization. In addition, comparisons are made between variable-speed pumping and fixed-speed pumping in multiobjective WTS optimization accounting for total cost and GHG emissions. The results show that the use of variable-speed pumping leads to significant savings in both total cost and GHG emissions from WTSs for the case study considered.

Multi-Objective Clustering with Data - and Human-Driven Metrics

While the usefulness of clustering analysis for knowledge discovery and information management is well accepted, it is difficult to objectively estimate the quality of clusters due to the fact that resulting clusters are heavily dependent on which and how many variables are used for clustering. Further, there are a number of heuristic criteria that can be used to estimate the quality of clusters and none of them completely dominates the others. To overcome these shortcomings, this study takes a multi-objective genetic algorithm (MOGA) approach to explore possible combinations of input variables to form clusters and evaluate cluster quality in a multi-objective space. In particular, each candidate cluster in MOGA is evolved and evaluated by both data-driven and human-driven metrics developed for this study. Note that human-driven metrics are introduced to measure managerial quality of clusters for efficient information management and effective decision making. The proposed system also allows the decision maker to navigate non-dominated cluster solutions and to choose one of them as the final solution. Experimental results on both synthetic and real data show promise in finding non-dominated cluster solutions with significant managerial insights for decision making.

Optimization of test interval for ageing equipment: A multi-objective genetic algorithm approach

Reducing the unavailability of safety systems at nuclear power plants, by utilizing the probabilistic safety assessment (PSA) methodology, is one of the prime goals in the nuclear industry. In that sense, optimization of test and maintenance activities, which are defined within the technical specifications, represents quite popular and interesting domain. Obtaining optimal test and maintenance schedule is of great significance for improving system availability and performance as well as plant availability in general. On the other side, equipment aging has gradually become a major concern in the nuclear industry since the number of safety systems components, that are approaching their wear-out stage, is rising fast. Nuclear power plants life management programs, considering safety components aging, are being developed and employed. The immense uncertainty associated to the available component aging rates databases poses significant difficulties in the process of incorporation and quantification of the aging effect within the PSA and, subsequently, in the decision-making process. In this paper, an approach for optimization of surveillance test interval of standby equipment with highly uncertain aging parameters, based on genetic algorithm technique and PSA, is presented. A standard standby safety system in nuclear power plant is selected as a case study. A Monte Carlo simulation-based approach is used to assess uncertainty propagation on system level. Optimal test interval is derived on the basis of minimal system unavailability and minimal impact of components aging parameters uncertainty. The results obtained in this application indicate the fact that risk-informed surveillance requirements differ from existing ones in technical specifications as well as show the importance of considering aging data uncertainties in component aging modeling.

Allocation of multi-type FACTS devices using multi-objective genetic algorithm approach for power system reinforcement

This paper presents a novel approach to find optimum locations and capacity of flexible alternating current transmission systems (FACTS) devices in a power system using a multi-objective optimization function. Thyristor controlled series compensator (TCSC) and static var compensator (SVC) are the utilized FACTS devices. Our objectives are: active power loss reduction, new introduced FACTS devices cost reduction, voltage deviation reduction, and increase on the robustness of the security margin against voltage collapse. The operational and controlling constraints, as well as load constraints, are considered in the optimum allocation. A multi-objective genetic algorithm (MOGA) is used to approach the Pareto-optimal front (non-dominated) solutions. In addition, the estimated annual load profile has been utilized in a sequential quadratic programming (SQP) optimization sub-problem to the optimum siting and sizing of FACTS devices. IEEE 14-bus Network is selected to validate the performance and effectiveness of the proposed method.

Multi-objective α-reliable path finding in stochastic networks with correlated link costs: A simulation-based multi-objective genetic algorithm approach (SMOGA)

In this study, we propose a new simulation-based multi-objective genetic algorithm (SMOGA) approach to find a portfolio of reliable nondominant (Pareto) paths, a set of paths that is equally good or better at least in one objective space compared to all other paths, in stochastic networks while considering link travel time uncertainties and correlations among link travel times. Our SMOGA model consists of a Monte Carlo simulation, a genetic algorithm, and a Pareto filter module to find a set of Pareto paths that minimize the travel time budgets required to satisfy multiple requirements of travel time reliability pre-determined by users. For our purposes, an alpha (and beta) reliable path finding problem is first formulated as a variant of Chance Constrained Multi-objective Programming (CCMOP) model. Then the simulation module is used to simulate stochastic networks with correlations among link travel times, and genetic algorithm and Pareto filter module are used to effectively search for Pareto paths that satisfy multiple reliability requirements in combinatorial solution space. Numerical results on the Chicago Sketch network demonstrate that our carefully designed genetic representation (a variable-length chromosome and two ways of generating initial population) and genetic operators (a crossover and a mutation operator) effectively explore solution space and ensure the feasibility and diversity of offspring paths. Further, our graphical representations of Pareto paths on the same network indicate that simplified models that do not consider correlations among link travel time distributions may find Pareto paths with a significant bias in travel time budgets and hence provide travelers sub-optimal paths.

A multi-objective genetic algorithm approach for adaptive PSS and TCSC-based controller design

This paper presents a multi-objective evolutionary algorithm approach for adaptive design of a power system stabiliser (PSS) and a thyristor controlled series compensator (TCSC)-based controller. The design objective is minimisation of both angle and voltage time trajectory deviations with respect to a post-contingency equilibrium point for a power system installed with a PSS and a TCSC controller. The optimal controller parameters of proposed controllers are coordinately designed. To show the adaptive capabilities of proposed approach, simulation results are presented at various loading conditions with parameter variations. Also, the results are compared with single objective GA approach to show the superiority of the proposed approach.

Process Planning and Scheduling in Distributed Manufacturing System Using Multiobjective Genetic Algorithm

AbstractThis paper proposed a multiobjective genetic algorithm approach for solving process planning and scheduling (PPS) problems in a distributed manufacturing system (DMS) environment. In this environment, factories processing various machines and tools at different geographical locations are often combined to produce various parts with different resource constraints. This paper proposed a fast multiobjective genetic algorithm with archive (fmoGA‐A) mechanism to deal with PPS problem with considering the minimization of the maximum total processing time and minimization of the maximum variation of workload of machine in DMS. The proposed algorithm has been compared with other approaches to testify and benchmark the optimization reliability on several PPS problems. These comparisons demonstrate the importance of PPS in DMS and indicate fmoGA‐A is better than adaptive weight approach genetic algorithm (awaGA) did on efficacy and efficient; the efficacy is not less than non‐dominated sorting genetic algorithm II (NSGA‐II) and strength Pareto evolutionary algorithm 2 (SPEA2) and the efficient is better than NSGA‐II and SPEA2. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning

The development of containers' transportation has maintained a high momentum since 1961, especially, the containers' traffic growth reached 10---11% in recent years. Container terminals play an important role in the transportation chain, in order to response rapidly for the requirement of modern logistics, better resource allocation, lower cost, and higher operation efficiency are needed. In this paper, we introduce the quay crane dynamic assignment (QCDA) in berth allocation planning problem (BAP) and formulate a multi-objective mathematical model considering each berth for container ship with QCDA and number of Quay Crane's Move. In order to solve this QCDA in BAP problem, we propose a multi-objective hybrid Genetic Algorithm approach with a priority-based encoding method. To demonstrate the effectiveness of proposed mohGA approach, numerical experiment is carried out and the best solution to the problem is obtained.

An ɛ-dominance-based multiobjective genetic algorithm for economic emission load dispatch optimization problem

In this paper, a novel multiobjective genetic algorithm approach for economic emission load dispatch (EELD) optimization problem is presented. The EELD problem is formulated as a non-linear constrained multiobjective optimization problem with both equality and inequality constraints. A new optimization algorithm which is based on concept of co-evolution and repair algorithm for handling non-linear constraints is presented. The algorithm maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ɛ-dominance. The use of ɛ-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto-set approximation by choosing an appropriate ɛ value. The proposed approach is carried out on the standard IEEE 30-bus 6-genrator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions of the multiobjective EELD problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem.