Hybridization of Adaptive Differential Evolution with BFGS

Robust face alignment and tracking by combining local search and global fitting

Previous studies have shown that embedding local search in classical evolutionary programming (EP) could lead to improved performance on function optimization problems. The utility of local search is investigated with fast evolutionary programming (FEP) and comparisons are offered between performance improvements obtained when using local search with Gaussian and Cauchy mutations. Experiments were conducted on a suite of four well known function optimization problems using two local search methods (conjugate gradient and F.J. Solis and R.J.-B. Wets, (1981)) with varying amounts of local search being incorporated into the evolutionary algorithm. Empirical results indicate that FEP with the conjugate gradient method outperforms other hybrid methods on three of the four functions when evolution was conducted for a fixed number of generations. Trials using local search produced solutions that were statistically as good as or better than trials without local search. However, the cost of using local search justified the enhancement in solution quality only when using Gaussian mutations but not when using Cauchy mutations.

MULTI-START LOCAL SEARCH FOR ONLINE SCHEDULING IN PARALLEL OPERATING THEATRE

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

Node placement problems have been long investigated in the optimization field due to numerous applications in facility location, logistics, services, etc. Such problems are attracting again the attention of researchers now from the networking domain, and more especially from Wireless Mesh Networks (WMNs) field. Indeed, the placement of mesh routers nodes appears to be crucial for the performance and operability of WMNs, in terms of network connectivity and stability. However, node placement problems are known for their hardness in solving them to optimality, and therefore heuristics methods are approached to near-optimally solve such problems. In this work we evaluate the performance of different heuristic methods in order to judge on their suitability of solving mesh router nodes problem. We have selected methods from two different families, namely, local search methods (Hill Climbing and Simulated Annealing) and population-based methods (Genetic Algorithms). The former are known for their capability to exploit the solution space by constructing a path of visited solutions, while the later methods use a population of individuals aiming to largely explore the solution space. In both cases, a bi-objective optimization consisting in the maximization of the size of the giant component in the mesh routers network (for measuring network connectivity) and that of user coverage are considered. In the experimental evaluation, we have used a benchmark of instances--varying from small to large size--generated using different distributions of mesh node clients (Uniform, Normal, Exponential and Weibull).

The authors describe a hybrid strategy for solving combinatorial optimisation problems, obtained by coupling a local search method to an evolutionary algorithm, and they provide an application to a particular variant of the vehicle routing problem. The local search method has been devised specifically for this class of problems. It is based on a composite neighbourhood, which is searched iteratively up to the point where no further improvements are made. The evolutionary structure is the niche search, an algorithm based on the evolution of several independent niches. Niches whose individuals' fitness is good remain, and the others tend to be replaced. The separation of the population into niches allows for a good compromise between intensive search (inside each niche) and diversification (through the separation between the niches). They also describe how they integrate specific problem knowledge into an evolutionary structure, in order to achieve a high performance optimisation algorithm. All the steps that they consider necessary are described in detail: finding an appropriate representation, determining what is a relevant neighbourhood, setting up a local search method and finally integrating the local search into an evolutionary algorithm.

Parameter identification is the process of constructing the mathematical model of a dynamic system based on measured dynamic responses. A non-classical search method namely genetic algorithm (GA) is employed in this study, which has several advantages over classical system identification techniques. Nevertheless, direct application of GA does not necessarily work well, particularly with regards to computational efficiency in fine-tuning when the solution approaches the optimal value. Thus, a local search (LS) method is introduced into the GA approach to improve this capability. Numerical example of a cantilever beam is presented to show the efficiency of the combined GA and LS method (GA-LS). The FE model of the cantilever beam is established with 10 beam elements. In this example, only the element properties associated with flapping motion are identified. The GA-LS method gives much better results than the GA method does. The mean error is reduced to 5.3% from 14.9% while to 14.5% from 47.2% for the maximum individual error. It is concluded that the GA-LS method is a more efficient search method than the GA method. The GA-LS method is then applied to identify the mass and stiffness parameters of a model helicopter blade using measured excitations and accelerations in this paper. Since the mass and stiffness of the model helicopter blade are not available, the only way to verify the identified parameters is to compare the ffequencies and mode shapes calculated from the identified parameters and the measured frequencies and modal shapes of the actual blade. The mean eiror of the frequencies is 0.9% and the maximum individual error 2.4%. The sequence of identified mode shapes is the same with measured mode shapes. Transactions on Engineering Sciences vol 43, © 2003 WIT Press, www.witpress.com, ISSN 1743-3533

This article presents a new multidimensional descent method for solving global optimization problems with box-constraints. This is a hybrid method where local search method is used for a local descent and global search is used for further multidimensional search on the subsets of intersection of cones generated by the local search method and the feasible region. The discrete gradient method is used for local search and the cutting angle method is used for global search. Two- and three-dimensional cones are used for the global search. Such an approach allows one, as a rule, to escape local minimizers which are not global ones. The proposed method is local optimization method with strong global search properties. We present results of numerical experiments using both smooth and non-smooth global optimization test problems. These results demonstrate that the proposed algorithm allows one to find a global or a near global minimizer.

Wireless Mesh Networks (WMNs) are an important networking infrastructure for providing cost-efficient broadband wireless connectivity to a group of users. WMNs are increasingly being used in urban areas, metropolitan and municipal area networks for deployment of medical, transport, surveillance systems, etc. The good performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical area to achieve network connectivity and stability. Therefore, optimal placement of router nodes in WMNs is a main issue in deployment of WMNs. In this work we propose and evaluate local search methods for placement of mesh routers in WMNs with a two fold objective: maximizing the size of the giant component in the network and user coverage. Given a grid area where to distribute a given number of mesh router nodes, which can have different radio coverage, and a number of fixed clients a priori distributed in the given area, local search methods explore different local movements and incrementally improve the quality of the router nodes placement in terms of network connectivity and user coverage. We have experimentally evaluated the considered local search methods through a benchmark of generated instances varying from small to large size. In the instances, different distributions of mesh clients (Uniform, Normal, Exponential and Weibull) are considered aiming to evaluate the quality of achieved solutions for different client distributions. Although local search methods can be stuck in local optima, the experimental evaluation showed their good performance; local search methods could thus be applied as a first choice for optimizing network connectivity and user coverage in WMNs.

Accurate prediction of oil consumption plays a dominant role in oil supply chain management. However, because of the effects of the coronavirus disease 2019 (COVID-19) pandemic, oil consumption has exhibited an uncertain and volatile trend, which leads to a huge challenge to accurate predictions. The rapid development of the Internet provides countless online information (e.g., online news) that can benefit predict oil consumption. This study adopts a novel news-based oil consumption prediction methodology–convolutional neural network (CNN) to fetch online news information automatically, thereby illustrating the contribution of text features for oil consumption prediction. This study also proposes a new approach called attention-based JADE-IndRNN that combines adaptive differential evolution (adaptive differential evolution with optional external archive, JADE) with an attention-based independent recurrent neural network (IndRNN) to forecast monthly oil consumption. Experimental results further indicate that the proposed news-based oil consumption prediction methodology improves on the traditional techniques without online oil news significantly, as the news might contain some explanations of the relevant confinement or reopen policies during the COVID-19 period.

Training of neural networks by local search such as gradient based algorithms could be difficult. This calls for the development of alternative training algorithms such as evolutionary search. However, training by evolutionary search often requires long computation time. The authors investigate the possibilities of reducing the time taken by combining the efforts of local search and evolutionary search. There are a number of approaches to combine these search strategies, but not all of them are successful. The paper provides a review of these approaches. Experimental results indicate that while the Baldwinian and the two-phase approaches are inefficient in improving the evolution process for difficult problems, the Lamarckian approach is able to speed up the training process. Moreover in the case where no local search method is appropriate for learning the desired task directly, the paper demonstrates that allowing the local search to learn another related task can assist the evolutionary search.

Aiming at the problem that the value of weighted matrix Q and R in linear quadratic optimal control (LQG) controller depends on the designer’s experience, adaptive differential evolution (JADE) algorithm with optional external archive is introduced to optimize the control parameters of LQG, so as to improve the performance of LQG controller. The dynamics model of 1/4 vehicle active suspension was established as the object, and the LQG objective function was constructed. The simulation experiments were carried out on B-class and C-class roads with the help of Matlab/Simulink.The experimental results show that the RMS values of the vertical acceleration of the vehicle body, the dynamic travel of the suspension and the dynamic displacement of the tire of the active suspension based on JADE optimized LQG controller under different road surfaces are reduced to a large extent compared with that of the passive suspension, which improves the stability of the vehicle and the comfort of the ride.

The goal of this research is to analyze how individual learning interacts with an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction, two learning models, implemented as local search procedures, are proposed. Experimental results show that, in highly irregular search spaces that are prone to premature convergence, local search methods are not an effective help to evolution. In addition, one interesting effect related to learning is reported: when the mutation rate is too high, learning acts as a repair, reintroducing some useful information that was lost.

Stochastic search methods can thoroughly explore the search space for the global optimum, but once the search approaches the optimum, they become inefficient to find the optimal solution precisely. Conversely, deterministic local methods are more efficient in finding the optimum precisely, particularly if a good initial estimate is given. In this work, the stochastic and local search methods are combined together for improving the search efficiency without losing reliability for solving multiobjective optimization problems. An improved multiobjective differential evolution (I-MODE) is used for the global search; it includes a termination criterion to decide the switching from the stochastic search to the local search. The normalized normal constraint method is employed to refine the non-dominated solutions obtained by the I-MODE search. The proposed hybrid approach is tested on several unconstrained and constrained biobjective optimization problems. It is then applied to the alkylation process, three-stage fermentation process integrated with extraction, and three-stage fermentation process integrated with pervaporation.

In recent years, multi-objective evolutionary optimization algorithms have shown success in different areas of research. Due to their efficiency and power, many researchers have concentrated on adapting evolutionary algorithms to generate Pareto solutions. This paper proposes a new memetic adaptive multi-objective evolutionary algorithm that is based on a three-term backpropagation network (MAMOT). This algorithm is an automatic search method for optimizing the parameters and performance of neural networks, and it relies on the use of the adaptive non-dominated sorting genetic algorithm-II integrated with the backpropagation algorithm, being used as a local search method. The presented MAMOT employs a self-adaptive mechanism toward improving the performance of the proposed algorithm and a local optimizer improving all the individuals in a population in order to obtain better accuracy and connection weights. In addition, it selects an appropriate number of hidden nodes simultaneously. The proposed method was applied to 11 datasets representing pattern classification problems, including two-class, multi-class and complex data reflecting real problems. Experiments were performed, and the results indicated that the proposed method is viable in pattern classification tasks compared to a multi-objective genetic algorithm based on a three-term backpropagation network (MOGAT) and some of the methods mentioned in the literature. The statistical analysis results of the t test and Wilcoxon signed-ranks test also show that the performance of the proposed method is significantly better than MOGAT.