Handling equality constraints in evolutionary optimization

Over the last few decades several methods have been proposed for handling functional constraints while solving optimization problems using evolutionary algorithms (EAs). However, the presence of equality constraints makes the feasible space very small compared to the entire search space. As a consequence, the handling of equality constraints has long been a difficult issue for evolutionary optimization methods. This paper presents a Hybrid Evolutionary Algorithm (HEA) for solving optimization problems with both equality and inequality constraints. In HEA, we propose a new local search technique with special emphasis on equality constraints. The basic concept of the new technique is to reach a point on the equality constraint from the current position of an individual solution, and then explore on the constraint landscape. We believe this new concept will influence the future research direction for constrained optimization using population based algorithms. The proposed algorithm is tested on a set of standard benchmark problems. The results show that the proposed technique works very well on those benchmark problems.     

Feasible directions algorithms for optimization problems with equality and inequality constraints

In this paper two algorithms, of the feasible-directions and dual feasible-directions type, are presented for optimization problems with equality and inequality constraints. An associated problem, having only inequality constraints, is defined, and shown to be equivalent to the original problem if a certain parameter is sufficiently large. The algorithms solve the associated problem, but incorporate a method for automatically increasing this parameter in order to ensure global convergence to a solution to the original problem. Any feasible directions algorithm can be similarly modified to enable it to handle equality constraints.Research sponsored by the US Army Research Office — Durham, Contract DAHCO4-73-C-0025 and the National Science Foundation Grant GK-37572.     

Optimal controls with equality state constraints

In this paper, we derive necessary conditions for a measurable controlu with values in a compact convex subsetU ofR ^P to minimize the integral $$\int_a^b {b^0 (s,x(s),u(s))} ds$$ subject to equality and inequality state constraints. The general conditions take the form of a maximum principle which involves an integration with respect to a finitely additive set function, but conditions are given under which this reduces to the classical maximum principle. These results are similar to results derived in Ref. 1 for relaxed controls.     

Optimizing two-level reverse distribution networks with hybrid memetic algorithms

In a Two-Level Reverse Distribution Network, products are returned from customers to manufacturers through collection and refurbishing sites. The costs of the reverse chain often overtake the costs of the forward chain by many times. With some known algorithms for the problem as reference, we propose a hybrid memetic algorithm that uses linear programming and a heuristic for defining routes. Moreover, we describe heuristics for deciding locations, algorithms to define routes for the products, and problem-specific genetic operators. Memetic algorithms have returned the best results for all instances.     

ODE methods for optimization with equality constraints

In an optimization problem with equality constraints we define an accessory function that is similar but different from a normal penalty function. In the accessory function we demonstrate the need to use small values of the parameter associated with an equality constraint. Large values of the parameter create extraneous stationary points which destroy the global convergence properties of steepest descent methods. By using small values of the parameters in the accessory function, when the current point is far away from the solution and when the constraint violations are large we are led to a refined version of the established SUMT method.     

Variations on memetic algorithms for graph coloring problems

Graph vertex coloring with a given number of colors is a well-known and much-studied NP-complete problem. The most effective methods to solve this problem are proved to be hybrid algorithms such as memetic algorithms or quantum annealing. Those hybrid algorithms use a powerful local search inside a population-based algorithm. This paper presents a new memetic algorithm based on one of the most effective algorithms: the hybrid evolutionary algorithm (HEA) from Galinier and Hao (J Comb Optim 3(4): 379–397, 1999). The proposed algorithm, denoted HEAD—for HEA in Duet—works with a population of only two individuals. Moreover, a new way of managing diversity is brought by HEAD. These two main differences greatly improve the results, both in terms of solution quality and computational time. HEAD has produced several good results for the popular DIMACS benchmark graphs, such as 222-colorings for \({<}{} \texttt {dsjc1000.9}{>}\), 81-colorings for \({<}{} \texttt {flat1000\_76\_0}{>}\) and even 47-colorings for \({<}{} \texttt {dsjc500.5}{>}\) and 82-colorings for \({<}{} \texttt {dsjc1000.5}{>}\).     

Bayesian comparison of models with inequality and equality constraints

A Bayesian model selection procedure for comparing models subject to inequality and/or equality constraints is proposed. An encompassing prior approach is used, and a general form of the Bayes factor of a constrained model against the encompassing model is derived. A simple estimation method is proposed which can estimate the Bayes factors for all candidate models simultaneously by using one set of samples from the encompassing model. A simulation study and a real data analysis demonstrate performance of the method.     

Second-order conditions for extremum problems with nonregular equality constraints

Combining results of Avakov about tangent directions to equality constraints given by smooth operators with results of Ben-Tal and Zowe, we formulate a second-order theory for optimality in the sense of Dubovitskii-Milyutin which gives nontrivial conditions also in the case of equality constraints given by nonregular operators. Secondorder feasible and tangent directions are defined to construct conical approximations to inequality and equality constraints which within a single construction lead to first- and second-order conditions of optimality for the problem also in the nonregular case. The definitions of secondorder feasible and tangent directions given in this paper allow for reparametrizations of the approximating curves and give approximating sets which form cones. The main results of the paper are a theorem which states second-order necessary condition of optimality and several corollaries which treat special cases. In particular, the paper generalizes the Avakov result in the smooth case.This research was supported by NSF Grant DMS-91-009324, NSF Grant DMS-91-00043, SIUE Research Scholar Award and Fourth Quarter Fellowship, Summer 1992.     

Second order sufficient conditions for optimizing with equality constraints

The paper establishes the equivalence to second order of the Jacobean (constrained gradient) and the Lagrangean approaches to optimizing a function subject to equality constraints. The second order equivalence does not seem to have been considered previously. It is established using a new second order sufficiency condition which has computational advantages over the other two.     

Locally Lipschitz vector optimization with inequality and equality constraints

The present paper studies the following constrained vector optimization problem: \(\mathop {\min }\limits_C f(x),g(x) \in - K,h(x) = 0\), where f: ? ⁿ → ? ^m , g: ? ⁿ → ? ^p are locally Lipschitz functions, h: ? ⁿ → ? ^q is C ¹ function, and C ? ? ^m and K ? ? ^p are closed convex cones. Two types of solutions are important for the consideration, namely w-minimizers (weakly efficient points) and i-minimizers (isolated minimizers of order 1). In terms of the Dini directional derivative first-order necessary conditions for a point x ⁰ to be a w-minimizer and first-order sufficient conditions for x ⁰ to be an i-minimizer are obtained. Their effectiveness is illustrated on an example. A comparison with some known results is done.     

A saddle-point criterion for convex problems with infinite-dimensional equality constraints

In this paper, we develop a saddle-point criterion for convex optimization problems with infinite-dimensional equality constraints. The method used in the derivation of this criterion is based on the property of openness of the equality operator. As an application, we develop necessary and sufficient conditions for an optimal control problem under a suitable controllability assumption. Constructing an optimal solution for a production planning problem is used as an illustrative example.The authors would like to thank Professor R. T. Rockafellar for his suggestions which have resulted in an improved version of the paper.     

Exact penalties for optimization problems with 2-regular equality constraints

A new first-order sufficient condition for penalty exactness that includes neither the standard constraint qualification requirement nor the second-order sufficient optimality condition is proposed for optimization problems with equality constraints.     

Global optimization of a quadratic functional with quadratic equality constraints

In this paper, we investigate a constrained optimization problem with a quadratic cost functional and two quadratic equality constraints. While it is obvious that, for a nonempty constraint set, there exists a global minimum cost, a method to determine if a given local solution yields the global minimum cost has not been established. We develop a necessary and sufficient condition that will guarantee that solutions of the optimization problem yield the global minimum cost. This constrained optimization problem occurs naturally in the computation of the phase margin for multivariable control systems. Our results guarantee that numerical routines can be developed that will converge to the global solution for the phase margin.     

Neighboring extremals of dynamic optimization problems with path equality constraints

Neighboring extremals of dynamic optimization problems with path equality constraints and with an unknown parameter vector are considered in this paper. With some simplifications, the problem is reduced to solving a linear, time-varying two-point boundary-value problem with integral path equality constraints. A modified backward sweep method is used to solve this problem. Two example problems are solved to illustrate the validity and usefulness of the solution technique. This research was supported in part by the National Aeronautics and Space Administration under NASA Grant No. NCC-2-106. The author is indebted to Professor A. E. Bryson, Jr., Department of Aeronautics and Astronautics, Stanford University, for many stimulating discussions.     

On the perturbation of least squares problems with equality constraints

Some new perturbation results are presented for least squares problems with equality constraints, in which relative errors are obtained on perturbed solutions, least squares residuals, and vectors of Lagrange multipliers of the problem, based on the equivalence of the problem to a usual least squares problem and optimal perturbation results for orthogonal projections.     

A primal-dual Newton-type algorithm for geometric programs with equality constraints

An augmented Lagrangian algorithm is used to find local solutions of geometric programming problems with equality constraints (GPE). The algorithm is Newton's method for unconstrained minimization. The complexity of the algorithm is defined by the number of multiplications and divisions. By analyzing the algorithm we obtain results about the influence of each parameter in the GPE problem on the complexity of an iteration. An attempt is made to estimate the number of iterations needed for convergence. In practice, certain hypotheses are tested, such as the influence of the penalty coefficient update formula, the distance of the starting point from the optimum, and the stopping criterion. For these tests, a random problem generator was constructed, many problems were run, and the results were analyzed by statistical methods.The authors are grateful to Dr. J. Moré, Argonne National Laboratory for his valuable comments.This research was partially funded by the Fund for the Advancement of Research at the Technion and by the Applied Mathematical Sciences Research Program (KC-04-02), Office of Energy Research, US Department of Energy, Contract No. W-31-109-Eng-38.     

Numerical solution of linear least-squares problems with linear equality constraints

In Ref. 1, a perturbation theory for the linear least-squares problem with linear equality constraints is presented. In this paper, the condition numbers of a general formula given in Ref. 1 are examined in order to compare them with the condition numbers of the two matrices of the problem. A class of test problems is also defined to study experimentally the numerical stability of three algorithms.This work was partially supported by the Ministero della Pubblica Istruzione, Rome, Italy.The authors thank the Centro Interdipartimentale di Calcolo Automatico e di Informatica Applicata of the University of Modena for having provided computing time on its VAX computer.     

Improved algorithms for several network location problems with equality measures

We consider single facility location problems with equity measures, defined on networks. The models discussed are, the variance, the sum of weighted absolute deviations, the maximum weighted absolute deviation, the sum of absolute weighted differences, the range, and the Lorenz measure. We review the known algorithmic results and present improved algorithms for some of these models.