Showing papers on "Multi-swarm optimization published in 1985"
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TL;DR: In this paper, a decomposition method for decomposing an optimization problem into a set of subproblems and a coordination problem that preserves coupling between the sub-problems is described.
Abstract: A method for decomposing an optimization problem into a set of subproblems and a coordination problem that preserves coupling between the subproblems is described The decomposition is achieved by separating the structural element optimization subproblems from the assembled structural optimization problem Each element optimization and optimum sensitivity analysis yields the cross-sectional dimensions that minimize a cumulative measure of the element constraint violation as a function of the elemental forces and stiffness The assembled structural optimization produces the overall mass and stiffness distributions optimized for minimum total mass subject to constraints that include the cumulative measures of the element constraint violations extrapolated linearly with respect to the element forces and stiffnesses The method is introduced as a special case of a multilevel, multidisciplinary system optimization and its algorithm is fully described for two-level optimization for structures assembled of finite elements of arbitrary type Numerical results are given as an example of a framework to show that the decomposition method converges and yields results comparable to those obtained without decomposition It is pointed out that optimization by decomposition should reduce the design time by allowing groups of engineers using different computers to work concurrently on the same large problem
228 citations
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TL;DR: This two-part study considers and improves several factors that affect the efficiency and robustness of the successive quadratic programming (SQP) optimization algorithm and describes several improvements to this infeasible-path approach.
159 citations
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TL;DR: In this paper, a chain-ruling algorithm is proposed to incorporate analytic derivative information for parts of the flowsheet and generally leads to less frequent evaluation of the flow-sheet modules.
40 citations
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TL;DR: It is shown that a number of seemingly unrelated nondiflerentiable optimization algorithms are special cases of two simple algorithm models: one for constrained and one for unconstrained optimization.
Abstract: It is shown that a number of seemingly unrelated nondiflerentiable optimization algorithms are special cases of two simple algorithm models: one for constrained and one for unconstrained optimization. In both of these models, the direction finding procedures use parametrized families of maps which are locally uniformly u.s.c. with respect to the generalized gradients of the functions defining the problem. The selection of the parameter is determined by a rule which is analogous to the one used in methods of feasible directions.
29 citations
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TL;DR: A new procedure for the solution of multi-constrained optimization problems based on the calculation of partial optima is proposed, designed to combine a high convergence speed together with a high reliability with respect to the avoidance of local minima.
5 citations