Infeasibility Driven Evolutionary Algorithm (IDEA) for Engineering Design Optimization

Singh, Hemant Kumar; Isaacs, Amitay; Ray, Tapabrata; Smith, Warren

doi:10.1007/978-3-540-89378-3_11

Cited by 43 publications

(38 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IDEA (Singh et al 2008) is a multiobjective evolutionary algorithm for dealing with constrained optimization problems. In Rada-Vilela et al (2013) we considered the use of IDEA to solve the conventional TSALBP.…”

Section: Adaptive Ideamentioning

confidence: 99%

See 1 more Smart Citation

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Chica

Bautista

Cordón

et al. 2016

Omega

View full text Add to dashboard Cite

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.

show abstract

Section: Adaptive Ideamentioning

confidence: 99%

“…The second method is a novel adaptive version of the Infeasibility Driven Evolutionary Algorithm (IDEA) (Singh et al 2008). The original IDEA version was explicitly designed for industrial constrained optimization problems such as ALB and was already applied to solve the TSALBP in Rada-Vilela et al (2013).…”

Section: Introductionmentioning

confidence: 99%

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Chica

Bautista

Cordón

et al. 2016

Omega

View full text Add to dashboard Cite

show abstract

“…(1) Infeasibility Driven Evolutionary Algorithm [2,8]: A novel approach is proposed which explicitly prefers marginally infeasible solutions over feasible solutions during the evolution. This preference translates into active search through feasible and infeasible regions of the search space leading to faster convergence towards optimum (which often lies on constraint boundary).…”

Section: Constraint Handlingmentioning

confidence: 99%

Development of optimization methods to deal with current challenges in engineering design optimization

Singh

2014

AIC

View full text Add to dashboard Cite

Abstract.Presented herein is a summary of PhD thesis entitled "Development of optimization methods to deal with current challenges in engineering design optimization" by the author. The thesis is available online at http://handle.unsw.edu.au/1959.4/ 51426. Keywords: Artificial intelligence, constraint handling, many-objective optimization, large scale optimization Background and motivationIn engineering design, optimization is customary, and often indispensable. Typical cases include minimization of drag for vehicles, minimization of weight for structures like buildings and bridges, maximization of power and lift for aircraft and rockets, minimization of fuel consumption for engines, etc. Therefore it comes as no surprise that development of fast and efficient optimization algorithms for engineering design is an actively pursued research area.In recent decades, metaheuristic algorithms have proven to be efficient, robust and versatile methods for numerical optimization. However, they usually need to evaluate a large number of candidate designs to find near optimum solutions. This becomes prohibitive for engineering optimization problems in which each design evaluation may require computationally expensive analysis and, consequently, the optimization process may take much longer time than affordable.Considering that the number of design evaluations is a critical factor in overall optimization time, it is imperative to develop techniques to search for the optimum design using fewest evaluations possible. With this singular goal, this thesis investigates a range of domains in which existing metaheuristic optimization approaches can be improved.Engineering problems are often highly non-linear, discontinuous, and non-differentiable, which rules out (or restricts) the applicability of analytical techniques for solving them. However, they exhibit additional attributes that prove challenging even to the existing metaheuristic techniques, thus making the search difficult and, consequently, creating a necessity for carrying out large numbers of evaluations. These include: (a) Constraints -constraints render a fraction (often a large fraction) of the search space infeasible, making it hard to find the optimum and at times even a feasible design; (b) Large number of objectivesPareto-dominance sorting, a commonly used technique in multi-objective optimization algorithms, is inadequate to solve problems with large numbers of objectives, a fact well reported in literature; (c) Large number of variables -the search space grows exponentially with the number of variables, which results in a corresponding increase in computational effort; and (d) Multiple models -for certain problems, there may be multiple candidate models to choose a solution from, with none of them being an obviously preferred one. In such a case, one may need to explore each one of them to find the global best.In this thesis, studies are conducted on each of these domains individually. Shortcomings of the existing 0921-7126/16/$35.00

show abstract

“…On the other hand, many real world optimisation problems have multiple constraints and, consequently, the need for satisfactory approaches for incorporating constraints arises. Previous studies have demonstrated the benefits of explicitly maintaining infeasible solutions among the candidate solutions for single-and multi-objective constrained optimization problems (Singh et al 2008;Ray et al 2009). For example, some recent evolutionary optimization algorithms that retain infeasible solutions until the last generation achieved good results in terms of the convergence rate and quality of solutions (Siew and Tanyimboh 2012b;Tanyimboh 2013, 2014).…”

Section: Introductionmentioning

confidence: 99%

Investigation into the Pressure-Driven Extension of the EPANET Hydraulic Simulation Model for Water Distribution Systems

Seyoum

Tanyimboh

2016

Water Resour Manage

View full text Add to dashboard Cite

Several hydraulic modelling approaches have been proposed previously to simulate pressure -deficient operating conditions in water distribution networks more realistically. EPANET-PDX is a pressure-driven extension of the EPANET 2 hydraulic simulation model that has an embedded logistic nodal head-flow function. The pressure-driven analysis algorithm in EPANET-PDX was investigated, to improve its performance under conditions of extremely low pressure. By integrating a line minimization procedure fully in the computational solution of the system of equations, the algorithm's consistency was improved by increasing its computational efficiency under conditions of extremely low pressure. The examples considered demonstrated that the pressure-driven analysis algorithm proposed is robust, computationally efficient, and the line minimization procedure is applied frequently. Overall, the results suggest that the algorithm is reliable. The formulation proposed is significantly faster than the previous model under conditions of extremely low pressure. The hydraulic and water quality modelling functionality of EPANET 2 was preserved. For the operating conditions with satisfactory pressure, where direct comparisons with EPANET 2 were possible, EPANET 2 was consistently faster.

show abstract

Infeasibility Driven Evolutionary Algorithm (IDEA) for Engineering Design Optimization

Cited by 43 publications

References 15 publications

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Development of optimization methods to deal with current challenges in engineering design optimization

Investigation into the Pressure-Driven Extension of the EPANET Hydraulic Simulation Model for Water Distribution Systems

Contact Info

Product

Resources

About