A Histogram Estimation of Distribution Algorithm for Reversible Lanes Optimization Problems

A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost.

Download Full-text

An Adaptive Covariance Scaling Estimation of Distribution Algorithm

Mathematics ◽

10.3390/math9243207 ◽

2021 ◽

Vol 9 (24) ◽

pp. 3207

Author(s):

Qiang Yang ◽

Yong Li ◽

Xu-Dong Gao ◽

Yuan-Yuan Ma ◽

Zhen-Yu Lu ◽

...

Keyword(s):

Optimization Problems ◽

Solution Space ◽

Optimization Methods ◽

Estimation Of Distribution Algorithm ◽

Distribution Model ◽

Selection Strategy ◽

Individual Selection ◽

Estimation Of Distribution ◽

The Mean ◽

Mean Vector

Optimization problems are ubiquitous in every field, and they are becoming more and more complex, which greatly challenges the effectiveness of existing optimization methods. To solve the increasingly complicated optimization problems with high effectiveness, this paper proposes an adaptive covariance scaling estimation of distribution algorithm (ACSEDA) based on the Gaussian distribution model. Unlike traditional EDAs, which estimate the covariance and the mean vector, based on the same selected promising individuals, ACSEDA calculates the covariance according to an enlarged number of promising individuals (compared with those for the mean vector). To alleviate the sensitivity of the parameters in promising individual selections, this paper further devises an adaptive promising individual selection strategy for the estimation of the mean vector and an adaptive covariance scaling strategy for the covariance estimation. These two adaptive strategies dynamically adjust the associated numbers of promising individuals as the evolution continues. In addition, we further devise a cross-generation individual selection strategy for the parent population, used to estimate the probability distribution by combing the sampled offspring in the last generation and the one in the current generation. With the above mechanisms, ACSEDA is expected to compromise intensification and diversification of the search process to explore and exploit the solution space and thus could achieve promising performance. To verify the effectiveness of ACSEDA, extensive experiments are conducted on 30 widely used benchmark optimization problems with different dimension sizes. Experimental results demonstrate that the proposed ACSEDA presents significant superiority to several state-of-the-art EDA variants, and it preserves good scalability in solving optimization problems.

Download Full-text

Multi-Objective Optimization with Estimation of Distribution Algorithm in a Noisy Environment

Evolutionary Computation ◽

10.1162/evco_a_00066 ◽

2013 ◽

Vol 21 (1) ◽

pp. 149-177 ◽

Cited By ~ 27

Author(s):

Vui Ann Shim ◽

Kay Chen Tan ◽

Jun Yong Chia ◽

Abdullah Al Mamun

Keyword(s):

Optimization Problems ◽

Estimation Of Distribution Algorithm ◽

Computational Time ◽

Decision Variable ◽

Objective Functions ◽

Multi Objective Optimization ◽

Noisy Environment ◽

Multi Objective ◽

Noisy Information ◽

Estimation Of Distribution

Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

Download Full-text

A histogram estimation of distribution algorithm for resource scheduling

Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18 ◽

10.1145/3205651.3205679 ◽

2018 ◽

Cited By ~ 1

Author(s):

Li-Tao Tan ◽

Wei-Neng Chen ◽

Jun Zhang

Keyword(s):

Resource Scheduling ◽

Estimation Of Distribution Algorithm ◽

Histogram Estimation ◽

Estimation Of Distribution

Download Full-text

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

Evolutionary Computation ◽

10.1162/1063656053583432 ◽

2005 ◽

Vol 13 (1) ◽

pp. 43-66 ◽

Cited By ~ 30

Author(s):

J. M. Peña ◽

J. A. Lozano ◽

P. Larrañaga

Keyword(s):

Bayesian Networks ◽

Unsupervised Learning ◽

Genetic Drift ◽

Optimization Problems ◽

Estimation Of Distribution Algorithm ◽

Estimation Of Distribution Algorithms ◽

Estimation Of Distribution ◽

Global Optima ◽

Effectiveness And Efficiency ◽

Distribution Algorithms

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

Download Full-text

Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher

The Scientific World JOURNAL ◽

10.1155/2014/836272 ◽

2014 ◽

Vol 2014 ◽

pp. 1-21

Author(s):

Kaifeng Yang ◽

Li Mu ◽

Dongdong Yang ◽

Feng Zou ◽

Lei Wang ◽

...

Keyword(s):

Multiobjective Optimization ◽

Optimization Problems ◽

Estimation Of Distribution Algorithm ◽

Distribution Model ◽

Test Problems ◽

Multiobjective Problems ◽

Estimation Of Distribution ◽

Distributed Solutions ◽

Accelerate Convergence ◽

Low Dimensional

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher andε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, sinceε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity,ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.

Download Full-text