A Bayesian Discrete Optimization Algorithm for Permutation Based Combinatorial Problems

Author(s):  
Jianming Zhang ◽  
Xifan Yao ◽  
Min Liu ◽  
Yan Wang
Author(s):  
Laurens Bliek ◽  
Sicco Verwer ◽  
Mathijs de Weerdt

Abstract When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables.


2019 ◽  
Vol 23 (3) ◽  
pp. 411-423 ◽  
Author(s):  
Xingfeng Wang ◽  
Qing Zhang ◽  
Xianrong Qin ◽  
Yuantao Sun

Performance-based design optimization of steel frames, with element sections selected from standard sections, is a computationally intensive task. In this article, an efficient discrete optimization algorithm is proposed for performance-based design optimization of steel frames. The computational efficiency is improved by searching in a sensible manner, guided by the deformation information of structural elements. To include all standard sections in the design space, the cross-sectional area ( Area) and moment of inertia ( Ix) are selected as the design variables. Based on different relationships between Area and Ix, a twofold strategy is put forward, which includes a quick exploration and an elaborate exploitation. For comparison, a similar algorithm is also proposed, using Area as the only design variable. A fixed relationship between Area and other sectional properties is used. Two numerical examples are presented to minimize the structural weight while satisfying performance constraints. The results indicate that the proposed discrete algorithm can achieve lighter structural designs than the area-only algorithm. Furthermore, the convergence history proves that a high computational efficiency can be realized by using the proposed algorithm.


2019 ◽  
Vol 52 (6) ◽  
pp. 945-959 ◽  
Author(s):  
Abdelazim G. Hussien ◽  
Aboul Ella Hassanien ◽  
Essam H. Houssein ◽  
Mohamed Amin ◽  
Ahmad Taher Azar

Author(s):  
Ricardo Sérgio Prado ◽  
Rodrigo César Pedrosa Silva ◽  
Frederico Gadelha Guimarães ◽  
Oriane M. Neto

The Differential Evolution (DE) algorithm is an important and powerful evolutionary optimizer in the context of continuous numerical optimization. Recently, some authors have proposed adaptations of its differential mutation mechanism to deal with combinatorial optimization, in particular permutation-based integer combinatorial problems. In this paper, the authors propose a novel and general DE-based metaheuristic that preserves its interesting search mechanism for discrete domains by defining the difference between two candidate solutions as a list of movements in the search space. In this way, the authors produce a more meaningful and general differential mutation for the context of combinatorial optimization problems. The movements in the list can then be applied to other candidate solutions in the population as required by the differential mutation operator. This paper presents results on instances of the Travelling Salesman Problem (TSP) and the N-Queen Problem (NQP) that suggest the adequacy of the proposed approach for adapting the differential mutation to discrete optimization.


2012 ◽  
Vol 150 ◽  
pp. 8-11
Author(s):  
Ying Hui Huang ◽  
Jian Sheng Zhang

This paper presents a discrete optimization algorithm based on a model of symbiosis, called binary symbiotic multi-species optimizer (BSMSO). BSMSO extends the dynamics of the canonical binary particle swarm algorithm (CBPSO) by adding a significant ingredient, which takes into account symbiotic co evolution between species. The BSMSO algorithm is evaluated on a number of discrete optimization problems for compared with the CBPSO algorithm. The comparisons show that on average, BSMSO outperforms the BPSOs in terms of accuracy and convergence speed on all benchmark functions.


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