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33
2007) Scatter Search for chemical and bioprocess optimization
 Journal of Global Optimization
"... Scatter search is a populationbased method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints such as the surrogate constr ..."
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Scatter search is a populationbased method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. In this paper, we develop a general purpose heuristic for a class of nonlinear optimization problems. The procedure is based on the scatter search methodology and treats the objective function evaluation as a black box, making the search algorithm contextindependent. Most optimization problems in the chemical and biochemical industries are highly nonlinear in either the objective function or the constraints. Moreover, they usually present differentialalgebraic systems of constraints. In this type of problem, the evaluation of a solution or even the feasibility test of a set of values for the decision variables is a timeconsuming operation. In this context, the solution method is limited to a reduced number of solution examinations. We have implemented a scatter search procedure in Matlab for this special class of difficult optimization problems. Our development goes beyond a simple exercise of applying scatter search to this class of problem, but presents innovative mechanisms to obtain a good balance between intensification and diversification in a shortterm search horizon. Computational comparisons with other recent methods over a set of benchmark problems favor the proposed procedure.
Constrained evolutionary optimization by approximate ranking and surrogate models, in
 Kabán & H.P. Schwefel, eds, ‘Proceedings of 8th Parallel Problem Solving From Nature (PPSN VIII
, 2004
"... Abstract. The paper describes an evolutionary algorithm for the general nonlinear programming problem using a surrogate model. Surrogate models are used in optimization when model evaluation is expensive. Two surrogate models are implemented, one for the objective function and another for a penalty ..."
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Abstract. The paper describes an evolutionary algorithm for the general nonlinear programming problem using a surrogate model. Surrogate models are used in optimization when model evaluation is expensive. Two surrogate models are implemented, one for the objective function and another for a penalty function based on the constraint violations. The proposed method uses a sequential technique for updating these models. The quality of the surrogate models is determined by their consistency in ranking the population rather than their statistical accuracy. The technique is evaluated on a number of standard test problems. 1
On the effects of adding objectives to plateau functions
 IEEE Transactions on Evolutionary Computation
, 2009
"... AbstractIn this paper, we examine how adding objectives to a given optimization problem affects the computational effort required to generate the set of Paretooptimal solutions. Experimental studies show that additional objectives may change the running time behavior of an algorithm drastically. ..."
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AbstractIn this paper, we examine how adding objectives to a given optimization problem affects the computational effort required to generate the set of Paretooptimal solutions. Experimental studies show that additional objectives may change the running time behavior of an algorithm drastically. Often it is assumed that more objectives make a problem harder as the number of different tradeoffs may increase with the problem dimension. We show that additional objectives, however, may be both beneficial and obstructive depending on the chosen objective. Our results are obtained by rigorous running time analyses that show the different effects of adding objectives to a wellknown plateau function. Additional experiments show that the theoretically shown behavior can be observed for problems with more than one objective. Index TermsMultiobjective optimization, running time analysis, theory. I. MOTIVATION I N RECENT YEARS, the number of publications on evolutionary multiobjective optimization has been rapidly growing; however, most of the studies investigate problems where the number of considered objectives is low, i.e., between two and four, while studies with many objectives are rare There is some evidence in the literature that additional objectives can make a problem harder. This discussion indicates that a general statement on the effect of increasing the number of objectives is not possible. For some problems, with a higher number of objectives it is more difficult to generate the Paretooptimal front; for other problems, it is easier. However, given the previous work, the question arises whether one and the same problem can be made both easier and harder depending on the added objective. This paper answers this question both experimentally and 10518215/$25.00
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
"... Abstract—In this paper, an adaptive tradeoff model (ATM) is proposed for constrained evolutionary optimization. In this model, three main issues are considered: 1) the evaluation of infeasible solutions when the population contains only infeasible individuals; 2) balancing feasible and infeasible so ..."
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Abstract—In this paper, an adaptive tradeoff model (ATM) is proposed for constrained evolutionary optimization. In this model, three main issues are considered: 1) the evaluation of infeasible solutions when the population contains only infeasible individuals; 2) balancing feasible and infeasible solutions when the population consists of a combination of feasible and infeasible individuals; and 3) the selection of feasible solutions when the population is composed of feasible individuals only. These issues are addressed in this paper by designing different tradeoff schemes during different stages of a search process to obtain an appropriate tradeoff between objective function and constraint violations. In addition, a simple evolutionary strategy (ES) is used as the search engine. By integrating ATM with ES, a generic constrained optimization evolutionary algorithm (ATMES) is derived. The new method is tested on 13 wellknown benchmark test functions, and the empirical results suggest that it outperforms or performs similarly to other stateoftheart techniques referred to in this paper in terms of the quality of the resulting solutions. Index Terms—Constrained optimization, evolutionary strategy (ES), multiobjective optimization, tradeoff model. I.
Constrained optimization via multiobjective evolutionary algorithms
 Deb (Eds.), Multiobjective Problems Solving from Nature: From Concepts to Applications, SpringerVerlag, Natural Computing Series, 2008, ISBN: 9783540729631
"... Summary. In this chapter, we present a survey of constrainthandling techniques based on evolutionary multiobjective optimization concepts. We present some basic definitions required to make this chapter selfcontained, and then we introduce the way in which a global (singleobjective) nonlinear opt ..."
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Summary. In this chapter, we present a survey of constrainthandling techniques based on evolutionary multiobjective optimization concepts. We present some basic definitions required to make this chapter selfcontained, and then we introduce the way in which a global (singleobjective) nonlinear optimization problem is transformed into an unconstrained multiobjective optimization problem. A taxonomy of methods is also proposed and each of them is briefly described. Some interesting findings regarding common features of the approaches analyzed are also discussed. 1
Handling Constraints for Search Based Software Test Data Generation
, 2008
"... A major issue in software testing is the automatic generation of the inputs to be applied to the programme under test. To solve this problem, a number of approaches based on search methods have been developed in the last few years, offering promising results for adequacy criteria like, for instance, ..."
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A major issue in software testing is the automatic generation of the inputs to be applied to the programme under test. To solve this problem, a number of approaches based on search methods have been developed in the last few years, offering promising results for adequacy criteria like, for instance, branch coverage. We devise branch coverage as the satisfaction of a number of constraints. This allows to formulate the test data generation as a constrained optimisation problem or as a constraint satisfaction problem. Then, we can see that many of the generators so far have followed the same particular approach. Furthermore, this constrainthandling point of view overcomes this limitation and opens the door to new designs and search strategies that, to the best of our knowledge, have not been considered yet. As a case study, we develop test data generators employing different penalty objective functions or multiobjective optimisation. The results of the conducted preliminary experiments suggest these generators can improve the performance of classical approaches.
Ecologyinspired evolutionary algorithm using feasibilitybased grouping for constrained optimization, in:
 Proc. Cong. Evolutionary Computation
, 2005
"... Abstract Different strategies for defining the relationship between feasible and infeasible individuals in evolutionary algorithms can provide with very different results when solving numerical constrained optimization problems. This paper proposes a novel EA to balance the relationship between fea ..."
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Abstract Different strategies for defining the relationship between feasible and infeasible individuals in evolutionary algorithms can provide with very different results when solving numerical constrained optimization problems. This paper proposes a novel EA to balance the relationship between feasible and infeasible individuals to solve numerical constrained optimization problems. According to the feasibility of the individuals, the population is divided into two groups, feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. Parents for reproduction are selected from the two groups by a novel parent selection method. The proposed method is tested using (l, k) evolution strategies with 13 benchmark problems. The results show that the proposed method improves the searching performance for most of the tested problems.
Niekerk, “Intelligent machine agent architecture for adaptive control optimization of manufacturing processes
 Advanced Engineering Informatics
"... a b s t r a c t Intelligent agents have been earmarked as the key enabling technology to provide the flexibility required by modern, competitive, customerorientated manufacturing environments. Rational agent behavior is of paramount importance when interacting with these environments to ensure sig ..."
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a b s t r a c t Intelligent agents have been earmarked as the key enabling technology to provide the flexibility required by modern, competitive, customerorientated manufacturing environments. Rational agent behavior is of paramount importance when interacting with these environments to ensure significant losses are not incurred. To achieve rationality, intelligent agents must constantly balance technical (process) and economic (enterprise wide) tradeoffs through cooperation, learning and autonomy. The research presented in this manuscript integrates methodological commonalities in intelligent manufacturing research and prognostics to design and evaluate a generic architecture for the core services of selflearning, rational, machining process regulation agents. The proposed architecture incorporates learning, flexibility and rational decision making through the integration of heterogeneous intelligent algorithms (i.e. neural networks and genetic algorithms) from fields such as machine learning, data mining and statistics. The architecture's ability to perceive, learn and optimize is evaluated on a highvolume industrial gun drilling process.
A Survey of ConstraintHandling Techniques Based on Evolutionary Multiobjective Optimization
"... Abstract. In this paper, we present several constrainthandling techniques based on evolutionary multiobjective optimization concepts. Some basic definitions are presented as well as the way in which a global nonlinear optimization problem is transformed into an unconstrained multiobjective optimiza ..."
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Abstract. In this paper, we present several constrainthandling techniques based on evolutionary multiobjective optimization concepts. Some basic definitions are presented as well as the way in which a global nonlinear optimization problem is transformed into an unconstrained multiobjective optimization problem. A taxonomy of methods is proposed and each one is described. Some interesting findings regarding common features of such approaches are also discussed. 1
Rionda. COPSO: Constrained Optimization via PSO algorithm
 Center
, 2007
"... This paper introduces the COPSO algorithm (Constrained Optimization via Particle Swarm Optimization) for the solution of single objective constrained optimization problems. The approach includes two new perturbation operators to prevent premature convergence, and a new ring neighborhood structure. ..."
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This paper introduces the COPSO algorithm (Constrained Optimization via Particle Swarm Optimization) for the solution of single objective constrained optimization problems. The approach includes two new perturbation operators to prevent premature convergence, and a new ring neighborhood structure. A constraint handling technique based on feasibility and sum of constraints violation, is equipped with an external file to store particles we termed “tolerant ”. The goal of the file is to extend the life period of those particles that otherwise would be lost after the adjustment of the tolerance of equality constraints. COPSO is applied to various engineering design problems, and for the solution of state of the art benchmark problems. Experiments show that COPSO is robust, competitive and fast.