This paper focuses on problems dealing with circular objects, being two- or three-dimensional. However, the search for exact local extrema is time consuming without any guarantee of a sufficiently good convergence to optimum.
One approach to solve this complex problem is to arrange the objects according to some prescribed order in and to search for the exact local extremum. When the geometric objects are objects, the packing problem reduces to a mathematical model whose constraints reflect the conditions of arrangement of objects within the given region, the mutual nonintersection conditions for objects, and any technological constraints that cannot be reduced to purely geometric constraints (as guillotine cuts for rectangular packing). The packing identifies the arrangement and positions of the geometric objects that determine the dimensions of the containing shape and reach the extremum of a specific objective function. Ĭutting and packing problems consist of packing a set of geometric objects/items of fixed dimensions and shape into a region of predetermined shape while accounting for the design and technological considerations of the problem. They are bottleneck problems in computer aided design where design plans are to be generated for industrial plants, electronic modules, nuclear and thermal plants, and so forth. They are encountered in a variety of real-world applications including production and packing for the textile, apparel, naval, automobile, aerospace, and food industries. They are very interesting NP-hard combinatorial optimization problems that is, no procedure is able to exactly solve them in deterministic polynomial time. IntroductionĬutting and Packing problems are scientifically challenging problems with a wide spectrum of applications.
Proofs, to branch-and-bound procedures, to constructive approaches, to multi-start nonconvex minimization, to billiard simulation, to multiphase heuristics, and metaheuristics. They have been tackled using various approaches-based algorithms ranging from computer-aided optimality These packing problems are NP hard optimization problems with a wide variety of applications. ASGO obtains configurations with smaller square containers on 63 instances at the same time it matches or approaches the current best results on the other five instances.This paper reviews the most relevant literature on efficient models and methods for packing circular objects/items into Euclidean plane regions where the objects/items and regions are either two- or three-dimensional. We compare the performance of ASGO on 68 benchmark instances at the Packomania website with the state-of-the-art results. Several other strategies, such as swapping two similar circles or swapping two circles in different quadrants in the container, are combined to increase the diversity of the configurations. The tabu strategy is used to prevent cycling and enhance the diversification during the search procedure. By adapting the action space defined for the rectangular packing problem, we approximate each circular item as a rectangular item, thus making it much easier to find comparatively larger vacant spaces for any given configuration. It finds configurations with the local minimum potential energy by the limited-memory BFGS (LBFGS) algorithm, then selects the circular items having the most deformations and moves them to some large vacant space or randomly chosen vacant space. Starting from several random configurations, ASGO runs the following potential descent method and basin-hopping strategy iteratively. This paper proposes an action-space-based global optimization (ASGO) approach for the problem of packing unequal circles into a square container such that the size of the square is minimized.
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