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Discrete optimization

About: Discrete optimization is a research topic. Over the lifetime, 4598 publications have been published within this topic receiving 158297 citations. The topic is also known as: discrete optimisation.


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Journal ArticleDOI
TL;DR: Monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules.
Abstract: In real world engineering design problems, decisions for design modifications are often based on engineering heuristics and knowledge. However, when solving an engineering design optimization problem using a numerical optimization algorithm, the engineering problem is basically viewed as purely mathematical. Design modifications in the iterative optimization process rely on numerical information. Engineering heuristics and knowledge are not utilized at all. In this article, the optimization process is analogous to a closed-loop control system, and a fuzzy proportional–derivative (PD) controller optimization engine is developed for engineering design optimization problems with monotonicity and implicit constraints. Monotonicity between design variables and the objective and constraint functions prevails in engineering design optimization problems. In this research, monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules. The designer only needs to define the initial values and move limits of the design variables to determine the parameters in the fuzzy PD controller optimization engine. In the optimization process using the fuzzy PD controller optimization engine, the function value of each constraint is evaluated once in each iteration. No sensitivity information is required. The fuzzy PD controller optimization engine appears to be robust in the various design examples tested.

57 citations

Journal ArticleDOI
TL;DR: In this paper, a novel metaheuristic optimization method, namely human behavior-based optimization (HBBO), is presented and it is shown that how it can be used for solving the practical optimization problems.
Abstract: Optimization techniques, specially evolutionary algorithms, have been widely used for solving various scientific and engineering optimization problems because of their flexibility and simplicity. In this paper, a novel metaheuristic optimization method, namely human behavior-based optimization (HBBO), is presented. Despite many of the optimization algorithms that use nature as the principal source of inspiration, HBBO uses the human behavior as the main source of inspiration. In this paper, first some human behaviors that are needed to understand the algorithm are discussed and after that it is shown that how it can be used for solving the practical optimization problems. HBBO is capable of solving many types of optimization problems such as high-dimensional multimodal functions, which have multiple local minima, and unimodal functions. In order to demonstrate the performance of HBBO, the proposed algorithm has been tested on a set of well-known benchmark functions and compared with other optimization algorithms. The results have been shown that this algorithm outperforms other optimization algorithms in terms of algorithm reliability, result accuracy and convergence speed.

57 citations

Journal ArticleDOI
TL;DR: This paper focuses on partially reduced SQP methods which are shown to be particularly well suited for optimization problems resulting from discretized DAE as well as fromDiscretized PDE.
Abstract: The solution of discretized optimization problems is a major task in many application areas from engineering and science. These optimization problems present various challenges which result from the high number of variables involved as well as from the properties of the underlying process to be optimized. They also provide several strucures which have to be exploited by efficient numerical solution approaches. In this paper we focus on partially reduced SQP methods which are shown to be particularly well suited for this problem class. In practical applications the efficiency of this approach is demonstrated for optimization problems resulting from discretized DAE as well as from discretized PDE. The practically important issues of inexact solution of linearized subproblems and of working range validation are tackled as well.

56 citations

Journal ArticleDOI
Mikio Sakai1
TL;DR: In this article, the authors describe an industrial application of the discrete element method (DEM) and present a coarse-grain DEM model for large-scale simulations, a signed distance function-based wall boundary model for complexly shaped walls and a DEM-moving particle semi-implicit method was developed for solid-liquid flow involving a free surface.
Abstract: In this paper, we describe an industrial application of the discrete element method (DEM). The DEM has been applied to various powder systems thus far and therefore appears to be an established approach. However, it cannot be applied to many industrial systems because of several critical problems such as modeling of large-scale simulations, complexly shaped wall boundaries and free surface fluid flow. To solve these problems, novel models were developed by our group. A coarse-grain DEM model was developed for large-scale simulations, a signed distance function-based wall boundary model was developed for complexly shaped walls and a DEM-moving particle semi-implicit method was developed for solid-liquid flow involving a free surface. The adequacy of these models was demonstrated through verification and validation tests. Our approach shows promise in industrial

56 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202313
202236
2021104
2020128
2019113
2018140