Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
Since estimation of distribution algorithms (EDAs) were proposed, many attempts have been made to improve EDAs’ performance in the context of global optimization. So far, the studies or applications of multivariate probabilistic model-based EDAs in continuous domain are still mostly restricted to low-dimensional problems. Traditional EDAs have difficulties in solving higher dimensional problems because of the curse of dimensionality and rapidly increasing computational costs. However, scaling up continuous EDAs for large-scale optimization is still necessary, which is supported by the distinctive feature of EDAs: because a probabilistic model is explicitly estimated, from the learned model one can discover useful properties of the problem. Besides obtaining a good solution, understanding of the problem structure can be of great benefit, especially for black box optimization. We propose a novel EDA framework with model complexity control (EDA-MCC) to scale up continuous EDAs. By employing weakly dependent variable identification and subspace modeling, EDA-MCC shows significantly better performance than traditional EDAs on high-dimensional problems. Moreover, the computational cost and the requirement of large population sizes can be reduced in EDA-MCC. In addition to being able to find a good solution, EDA-MCC can also provide useful problem structure characterizations. EDA-MCC is the first successful instance of multivariate model-based EDAs that can be effectively applied to a general class of up to 500-D problems. It also outperforms some newly developed algorithms designed specifically for large-scale optimization. In order to understand the strengths and weaknesses of EDA-MCC, we have carried out extensive computational studies. Our results have revealed when EDA-MCC is likely to outperform others and on what kind of benchmark functions.
Advertisement: IEEE Xplore digital library. Driving research at the world’s leading universities and institutions.
In engineering design and manufacturing optimization, the trade-off between a quality performance metric and the probability of satisfying all performance specifications (yield) of a product naturally leads to a chance-constrained bi-objective stochastic optimization problem (CBSOP). A new method, called MOOLP (multi-objective uncertain optimization with ordinal optimization (OO)), Latin supercube sampling and parallel computation), is proposed in this paper for dealing with the CBSOP. This proposed method consists of a constraint satisfaction phase and an objective optimization phase. In its constraint satisfaction phase, by using the OO technique, an adequate number of samples are allocated to promising solutions, and the number of unnecessary MC simulations for noncritical solutions can be reduced. This can achieve more than five times speed enhancement compared to the application of using an equal number of samples for each candidate solution. In its MOEA/D-based objective optimization phase, by using LSS, more than five times speed enhancement can be achieved with the same estimation accuracy compared to primitive MC simulation. Parallel computation is also used for speedup. A real-world problem of the bi-objective variation-aware sizing for an analog integrated circuit is used in this paper as a practical application. The experiments clearly demonstrate the advantages of MOOLP.
Fitness modeling has received growing interest from the evolutionary computation community in recent years. With a fitness model, one can improve evolutionary algorithm efficiency by directly sampling new solutions, developing hybrid guided evolutionary operators or using the model as a surrogate for an expensive fitness function. This paper addresses several issues on fitness modeling of discrete functions, particularly how modeling quality and efficiency can be improved. We define the Markov network fitness model in terms of Walsh functions. We explore the relationship between the Markov network fitness model and fitness in a number of discrete problems, showing how the parameters of the fitness model can identify qualitative features of the fitness function. We define the fitness prediction correlation, a metric to measure fitness modeling capability of local and global fitness models. We use this metric to investigate the effects of population size and selection on the tradeoff between model quality and complexity for the Markov network fitness model.
Estimation of distribution algorithms are gaining increased research interest due to their advantage in exploiting linkage information. This paper examines the sampling techniques of a restricted Boltzmann machine-based multi-objective (MO) estimation of distribution algorithm (REDA). The behaviors of the sampling techniques in terms of energy levels are rigorously investigated, and a sampling mechanism that exploits the energy information of the solutions in a trained network is proposed to improve the search capability of the algorithm. The REDA is then hybridized, with a genetic algorithm and a local search based on an evolutionary gradient approach, to enhance the exploration and exploitation capabilities of the algorithm. Thirty-one benchmark test problems, which consist of different difficulties and characteristics, are used to examine the efficiency of the proposed algorithm. Empirical studies show that the proposed algorithm gives promising results in terms of inverted generational distance and nondominance ratio in most of the test problems.
Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current literature tackle this problem by means of automatic intelligent systems, and, when they do, they focus on limited areas with elementary traffic light schedules. In this paper, we propose an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs. The solutions obtained are simulated with simulator of urban mobility, a well-known microscopic traffic simulator. For this study, we have tested two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Bahía Blanca in Argentina (American style) and Málaga in Spain (European style). Our algorithm is shown to obtain efficient traffic light cycle programs for both kinds of cities. In comparison with expertly predefined cycle programs (close to real ones), our PSO achieved quantitative improvements for the two main objectives: 1) the number of vehicles that reach their destination and 2) the overall journey time.
This paper proposes multiobjective particle swarm optimization with preference-based sort (MOPSO-PS), in which the user’s preference is incorporated into the particle swarm optimization (PSO) update process to determine the relative merits of nondominated solutions while handling the mutual dependences and priorities of objectives. In MOPSO-PS, the user’s preference is represented as the degree of consideration for each objective using the fuzzy measure. The global evaluation of a particle, which represents the quality of the particle according to the user’s preference, is carried out by the fuzzy integral, which integrates the partial evaluation value of each objective with respect to the degree of consideration. Since the global best attractor of each particle in the population is randomly chosen among the nondominated particles having a relatively higher global evaluation value in each PSO update iteration, the optimization is gradually guided by the user’s preference. After the optimization, the most preferable particle can be chosen for practical use by selecting the particle with the highest global evaluation value. The effectiveness of the proposed MOPSO-PS is demonstrated by the application of path, following footstep optimization for humanoid robots in addition to empirical comparison with the other algorithms. The footsteps optimized by the MOPSO-PS were verified by simulation. The results indicate that the user’s preference is properly reflected in optimized solutions without any loss of overall solution quality or diversity.
Provides a listing of current committee members and society officers.
This paper investigates the mean- and parent-centric balance in real-valued crossover operators, which is strongly related to the powerful and efficient optimization performance. To treat the property as a continuous value, a novel crossover operator, called asymmetrical normal distribution crossover (ANDX), has been introduced. Because the crossover operator has a tunable parameter for the mean- and parent-centric balance, an arbitrary continuous balance is achievable, whereas in previous studies, the property has been treated dualistically. Through numerically empirical analysis with ANDX, the relationship between optimization performance and balance was clearly observed by changing the balance at regular intervals. To determine a practically suitable first choice of balance, a performance comparison with various parameter settings of ANDX was conducted on large-scale objective functions. The experimental results demonstrate that we should consider accepting mean-centric crossover operators as a realistic first choice in practice.