Direct Local Search Research Articles

Combine the AGA and L-M algorithms to predict the oil-water flow profile of production wells

Production logging is the main means of dynamic monitoring of oil wells at present. The interpretation method of production logging fluid profiles is the key to the dynamic evaluation of oil wells and is of great significance. A joint optimization technique of a AGA and L-M is proposed to deal with the profile data of oil-water two-phase flow. The method is based on the energy conservation of the fluid in the production unit space. According to the nonlinear weighted least squares principle and the error theory, the objective function of the minimum value is established by the difference between the theoretical fluid temperature and the measured value of the thermometer. According to the fluid volume conversion, the flow rate of each phase in the well and the flow log value at the bottom of the control body are constrained. In order to improve the interpretation accuracy, the global search method (AGA) of genetic algorithm and the direct local search method of L-M algorithm are combined to solve the optimal solution. Firstly, the adaptive genetic algorithm is used for heuristic global search, and then as a result of the initial parameters, the L-M method is applied to approach the optimal solution. The interpretation results from the joint inversion are stable and reliable. This method is not only suitable for vertical wells but also for inclined wells.

A deterministic method for continuous global optimization using a dense curve

In this paper, we develop a new approach for solving a large class of global optimization problems for objective functions which are only continuous on a rectangle of Rn. This method is based on the reducing transformation technique by running in the feasible domain a single parametrized Lissajous curve, which becomes increasingly denser and progressively fills the feasible domain. By means of the one-dimensional Evtushenko algorithm, we realize a mixed method which explores the feasible domain. To speed up the mixed exploration algorithm, we have incorporated a DIRECT local search type algorithm to explore promising regions. This method converges in a finite number of iterations to the global minimum within a prescribed accuracy ε>0. Simulations on some typical test problems with diverse properties and different dimensions indicate that the algorithm is promising and competitive.

Multi-objective airfoil shape optimization using an adaptive hybrid evolutionary algorithm

In this study, multi-objective aerodynamic optimization problems were conducted with a hybrid evolutionary-adaptive directional local search method for convergence enhancement. The directional search operator includes selection of search direction and one-dimensional search. Probability for the directional operator is adaptively changed based on relative effectiveness of the directional search operator and evolutionary operators such as crossover and mutation. The adaptive directional operator is combined with a baseline evolutionary multi-objective algorithm (EMOA) such as NSGA-II or MOGA. Multi-objective airfoil shape optimization examples are defined as drag minimization/lift maximization and L/D maximization at high lift and cruise conditions in subsonic and transonic regimes. The CST method and the B-spline method were used for airfoil shape parameterization. Design examples with drag minimization and L/D maximization at cruise conditions are all found to have uni-modal design spaces, and the local search operator is effective for those examples. However, lift maximization and high angle of attack cases show multi-modality in the design spaces due to flow separations and thus the local search is not effective for those cases. Design results show that the adaptive directional search method significantly enhances convergence of problems in which the directional search is effective, and also minimizes unnecessary spending of computational budget for cases in which the directional search does not produce competitive solutions. The present method improves search performance for different airfoil parameterization methods and different baseline EMOAs. Statistical tests confirm that the adaptive hybrid method is superior to the baseline EMOA.

Large scale continuous global optimization based on micro differential evolution with local directional search

Over the years, many optimization algorithms have been developed to solve large-scale optimization problems accurately and efficiently. In this regard, Memetic Algorithms offer robust and efficient framework that hybridizes the Evolutionary Algorithms with a local heuristic search. In this work, we propose micro Differential Evolution with a Directional Local Search (µDSDE) algorithm using a small population size to solve large scale continuous optimization problems. In this technique, the best individual retains its position, the second best individual undergoes mutation and crossover processes of DE, and the rest are reinitialized on the search space. Exploration of the search is carried out with the dispersal of the worst individuals whereas exploitation is performed through DE operators and Directional Local Search (DLS). We conducted extensive empirical studies using two test suites on Large Scale Global Optimization benchmark with up to 5000 dimensions. The results show that µDSDE considerably outperforms existing solutions in terms of the convergence rate and solution quality.

A cost-benefit local search coordination in multimeme differential evolution for constrained numerical optimization problems

Abstract This paper introduces an adaptive local search coordination for a multimeme Differential Evolution to constrained numerical optimization problems. The proposed approach associates a pool of direct local search operators within the standard Differential Evolution. The coordination mechanism consists of a probabilistic method based on a cost-benefit scheme, and it is aimed to regulate the activation probability of every local search operator during the evolutionary cycle of the global search. Also, the method adopts the ɛ -constrained method as a constraint-handling technique. The proposed approach is tested on thirty-six well-known benchmark problems. Numerical results show that the proposed method is suitable to coordinate a set of local search operators adequately within a memetic scheme for constrained search spaces.

A Direct Local Search Method and its Application to a Markovian Model

While the hidden mixture transition distribution (HMTD) model is a powerful framework for the description, analysis, and classification of longitudinal sequences of continuous data, it is notoriously difficult to estimate because of the complexity of its solution space. In this paper, we explore how a new heuristic specifically developed for the HMTD performs compared to different standard optimization algorithms. This specific heuristic can be classified as a hill-climbing method, and different variants are proposed, including a jittering procedure to escape local maxima and measures to speed up the convergence. Different popular approaches are used for comparison, including PSO, SA, GA, NM, L-BFGS-B, and DE. The same HMTD model was optimized on different datasets and the results were compared in terms of both fit to the data and estimated parameters. Even if the complexity of the problem implies that no one algorithm can be considered as an overall best, our heuristic performed well in all situations, leading to useful solutions in terms of both fit and interpretability.

A global optimization algorithm for trajectory data based car-following model calibration

How to calibrate the parameters of car-following models based on observed traffic data is a vital problem in traffic simulation. Usually, the core of calibration is cast into an optimization problem, in which the decision variables are car-following model parameters and the objective function usually characterizes the difference between empirical vehicle movements and their simulated correspondences. Since the objective function is usually nonlinear and non-convex, various greedy or stochastic algorithms had been proposed during the last two decades. However, the performance of these algorithms remains to be further examined. In this paper, we revisit this important problem with a special focus on the geometric feature of the objective function. First, we prove that, from a global perspective, most existing objective functions are Lipschitz continuous. Second, we show that, from a local perspective, many of these objective functions are relatively flat around the global optimal solution. Based on these two features, we propose a new global optimization algorithm that integrates global direct search and local gradient search to find the optimal solution in an efficient manner. We compare this new algorithm with several existing algorithms, including Nelder–Mead (NM) algorithm, sequential quadratic programming (SQP) algorithm, genetic algorithm (GA), and simultaneous perturbation stochastic approximation (SPSA) algorithm, on NGSIM trajectory datasets. Results demonstrate that the proposed algorithm has a fast convergence speed and a high probability of finding the global optimal solution. Moreover, it has only two major configuration parameters that can be easily determined in practice.

Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems

Evolutionary algorithms (EAs) are often employed to multiobjective optimization, because they process an entire population of solutions which can be used as an approximation of the Pareto front of the tackled problem. It is a common practice to couple local search with evolutionary algorithms, especially in the context of combinatorial optimization. In this paper a new local search method is proposed that utilizes the knowledge concerning promising search directions. The proposed method can be used as a general framework and combined with many methods of iterating over a neighbourhood of an initial solution as well as various decomposition approaches. In the experiments the proposed local search method was used with an EA and tested on 2-, 3- and 4-objective versions of two well-known combinatorial optimization problems: the travelling salesman problem (TSP) and the quadratic assignment problem (QAP). For comparison two well-known local search methods, one based on Pareto dominance and the other based on decomposition, were used with the same EA. The results show that the EA coupled with the directional local search yields better results than the same EA coupled with any of the two reference methods on both the TSP and QAP problems.

The multi-objective generalized consistent vehicle routing problem

More and more companies in the routing industry are providing consistent service to gain competitive advantage. However, improved service consistency comes at the price of higher routing cost, i.e., routing cost and service consistency are conflicting objectives. In this paper, we extend the generalized consistent vehicle routing problem (GenConVRP) by considering several objective functions: improving driver consistency and arrival time consistency, and minimizing routing cost are independent objectives of the problem. We refer to the problem as the multi-objective generalized consistent vehicle routing problem (MOGenConVRP). A multi-objective optimization approach enables a thorough trade-off analysis between the conflicting objective functions. The results of this paper should help companies in finding adequate consistency goals to aim for. Results are generated for several test instances by two exact solution approaches and one heuristic. The exact approaches are based on the ϵ-constraint framework and are used to solve small test instances to optimality. Large instances with up to 199 customers and a planning horizon of 5 days are solved by multi directional large neighborhood search (MDLNS) that combines the multi directional local search framework and the LNS for the GenConVRP. The solution quality of the heuristic is evaluated by examining five multi-objective quality indicators. We find that MDLNS is an eligible solution approach for performing a meaningful trade-off analysis.Our analysis shows that a 70 percent better arrival time consistency is achieved by increasing travel cost by not more than 3.84 percent, on average; visiting each customer by the same driver each time is significantly more expensive than allowing at least two different drivers per customer; in many cases, arrival time consistency and driver consistency can be improved simultaneously.

Inverse hydrograph routing optimization model based on the kinematic wave approach

This article presents and validates the inverse flood hydrograph routing optimization model under kinematic wave (KW) approximation in order to produce the upstream (inflow) hydrograph, given the downstream (outflow) hydrograph of a river reach. The cost function involves minimization of the error between the observed outflow hydrograph and the corresponding directly routed outflow hydrograph. Decision variables are the inflow hydrograph ordinates. The KW and genetic algorithm (GA) are coupled, representing the selected methods of direct routing and optimization, respectively. A local search technique is also enforced to achieve better agreement of the routed outflow hydrograph with the observed hydrograph. Computer programs handling the direct flood routing, cost function and local search are linked with the optimization model. The results show that the case study inflow hydrographs obtained by the GA were reconstructed with accuracy. It was also concluded that the coupled KW-GA model framework can perform inverse hydrograph routing with numerical stability.

Adaptive directional local search strategy for hybrid evolutionary multiobjective optimization

A novel adaptive local search method is developed for hybrid evolutionary multiobjective algorithms (EMOA) to improve convergence to the Pareto front in multiobjective optimization. The concepts of local and global effectiveness of a local search operator are suggested for dynamic adjustment of adaptation parameters. Local effectiveness is measured by quantitative comparison of improvements in convergence made by local and genetic operators based on a composite objective. Global effectiveness is determined by the ratio of number of local search solutions to genetic search solutions in the nondominated solution set. To be consistent with the adaptation strategy, a new directional local search operator, eLS (efficient Local Search), minimizing the composite objective function is designed. The search direction is determined using a centroid solution of existing neighbor solutions without making explicit calculations of gradient information. The search distance of eLS decreases adaptively as the optimization process converges. Performances of hybrid methods NSGA-II+eLS are compared with the baseline NSGA-II and NSGA-II+HCS1 for multiobjective test problems, such as ZDT and DTLZ functions. The neighborhood radius and local search probability are selected as adaptation parameters. Results show that the present adaptive local search strategy can provide significant convergence enhancement from the baseline EMOA by dynamic adjustment of adaptation parameters monitoring the properties of multiobjective problems on the fly.

An improved approach to the efficient construction of and search operations in motion graphs

Motion graphs have been widely used as an effective technique in the synthesis of human motions. To synthesize well-formed motions, motion graphs require smooth transitions and good connectivity. However, the time and costs grow quadratically as the graph size increases. In this paper, we present a method to set up a hierarchical structure, called a structure table. The coarse level of this graph models different motion categories and their transitions, and the fine level mainly captures different motion styles. An interpolation space method is used to improve the graph connectivity at the fine level. This structure guides the establishment of a faster and more realistic graphical object. After the building process, this structure also helps with searching the motion graph, by performing a traverse search and placing the findings into a direct look-up table or local search depository. Moreover, when there is no direct link between the different motions, a navigation map is introduced to conduct an appropriate search path via a series of transition motion clips. The experimental results indicate that our approach could lower the computation time in construction and searching, while simultaneously providing a richer variety of motion styles.

Optimization of multiple edge barriers with genetic algorithms coupled with a Nelder–Mead local search

This research work aims at developing a new multi-criteria optimization method dedicated to complex road noise barriers. Numerical simulations of the acoustical propagation have been achieved using MICADO, a 2D boundary element method (BEM) code developed at CSTB. The optimization part is carried out with the help of a Nelder–Mead algorithm (direct local search method) coupled with an evolutionary strategy in order to globalize the approach. A first application of this combination between an outdoor sound propagation numerical code and an optimization algorithm concerns the optimization of noise barrier caps with the following varying parameters: the cap size, its shape and its surface impedance. The cost function to be minimized is defined through a mean value of the insertion loss due to the added crowning compared to the straight, rigid barrier solution of same overall height, averaged on several receiver points within the barrier shadow zone. Final results show a significant improvement of the efficiency of a multiple edge noise barrier by optimizing values of both size and impedance.

Local Search Efficiency when Optimizing Unimodal Pseudoboolean Functions

This work is a continuation of our previous papers devoted to exploration of the regular search procedures efficiency in binary search spaces. Here we formulate the problem in a rather general form as a problem of optimization of an unimodal pseudoboolean function given implicitly and obtain analitical estimates of the expected time of a minimum point search for procedures of direct local search. These estimates are polinomial for the case of weakly nonmonotone functions and exponential for the general case of arbitrary unimodal functions. We hope that the proposed result will be usefull first of all for practical applications.