Grid-Based Hybrid Genetic Approach to Relaxed Flexible Flow Shop with Sequence-Dependent Setup Times

In this paper, a hybrid genetic algorithm implemented in a grid environment to solve hard instances of the flexible flow shop scheduling problem with sequence-dependent setup times is introduced. The genetic algorithm takes advantage of the distributed computing power on the grid to apply a hybrid local search to each individual in the population and reach a near optimal solution in a reduced number of generations. Ant colony systems and simulated annealing are used to apply a combination of iterative and cooperative local searches, respectively. This algorithm is implemented using a master–slave scheme, where the master process distributes the population on the slave process and coordinates the communication on the computational grid elements. The experimental results point out that the proposed scheme obtains the upper bound in a broad set of test instances. Also, an efficiency analysis of the proposed algorithm indicates its competitive use of the computational resources of the grid.

An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis

Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.

Ways to improve the reliability of interpreting the results of geophysical surveys performed for the local forecast and prospecting for solid minerals: a geodynamic approach

Background. The geodynamic approach is effectively used in regional forecasting and prospecting works. However, its application for local forecasting and prospecting for solid minerals is limited and sometimes impossible. One of the key problems of local forecasting and prospecting for minerals (solid, liquid, gaseous) is the presence of non-standard (flickering) geophysical anomalies, which complicates the interpretation of the results of geophysical surveys performed at different times at different stages of geological exploration. The article is devoted to clarifying the possibility of using geodynamic research in local forecasting and prospecting for solid minerals on the basis of attracting the latest scientific knowledge from the field of studying the spatio-temporal patterns of the development of modern geodynamic processes and their influence on the Earth’s geophysical fields. Aim. To increase the reliability of interpreting the results of geophysical surveys performed for local forecasting and prospecting for solid minerals.Materials and methods. The research was carried out on the basis of a comprehensive analysis of literature data, fund materials and the results of many years of the author’s own research in the fields of modern geodynamics and prospecting and exploration geodynamics. The initial data were based on the monitoring data of various Earth’s geophysical fields (deformation, seismic, and surface magnetic).Results. A geodynamic reason for the appearance of flickering anomalies in the Earth’s geophysical fields (in particular, magnetic) has been established, and a mechanism for their formation under the influence of modern geodynamic processes has been proposed. The possibility of using the geodynamic approach in the prospecting for solid minerals has been expanded, and ways to increasing the efficiency of local searches have been proposed.Conclusions. The research demonstrates the possibility of using geodynamic studies in local prospecting for solid minerals, which helps to increase the reliability of the results of interpretation of geophysical survey data, and, as a result, to reduce the overall financial and time costs involved with searching for mineral deposits.

Multiple infill criterion-assisted hybrid evolutionary optimization for medium-dimensional computationally expensive problems

Surrogate-assisted evolutionary algorithms have been paid more and more attention to solve computationally expensive problems. However, model management still plays a significant importance in searching for the optimal solution. In this paper, a new method is proposed to measure the approximation uncertainty, in which the differences between the solution and its neighbour samples in the decision space, and the ruggedness of the objective space in its neighborhood are both considered. The proposed approximation uncertainty will be utilized in the surrogate-assisted global search to find a solution for exact objective evaluation to improve the exploration capability of the global search. On the other hand, the approximated fitness value is adopted as the infill criterion for the surrogate-assisted local search, which is utilized to improve the exploitation capability to find a solution close to the real optimal solution as much as possible. The surrogate-assisted global and local searches are conducted in sequence at each generation to balance the exploration and exploitation capabilities of the method. The performance of the proposed method is evaluated on seven benchmark problems with 10, 20, 30 and 50 dimensions, and one real-world application with 30 and 50 dimensions. The experimental results show that the proposed method is efficient for solving the low- and medium-dimensional expensive optimization problems by compared to the other six state-of-the-art surrogate-assisted evolutionary algorithms.

A Scalable, Black-box, Hybrid Genetic Algorithm for Continuous Multimodal Optimization in Moderate Dimensions

Optimization problems can be found in many areas of science and technology. Often, not only the global optimum, but also a (larger) number of near-optima are of interest. This gives rise to so-called multimodal optimization problems. In most of the cases, the number and quality of the optima is unknown and assumptions on the objective functions cannot be made. In this paper, we focus on continuous, unconstrained optimization in moderately high dimensional continuous spaces (<=10). We present a scalable algorithm with virtually no parameters, which performs well for general objective functions (non-convex, discontinuous). It is based on two well-established algorithms (CMA-ES, deterministic crowding). Novel elements of the algorithm are the detection of seed points for local searches and collision avoidance, both based on nearest neighbors, and a strategy for semi-sequential optimization to realize scalability. The performance of the proposed algorithm is numerically evaluated on the CEC2013 niching benchmark suite for 1-20 dimensional functions and a 9 dimensional real-world problem from constraint optimization in climate research. The algorithm shows good performance on the CEC2013 benchmarks and falls only short on higher dimensional and strongly inisotropic problems. In case of the climate related problem, the algorithm is able to find a high number (150) of optima, which are of relevance to climate research. The proposed algorithm does not require special configuration for the optimization problems considered in this paper, i.e. it shows good black-box behavior.

Clutter Suppression Method for Off-Grid Effects Mitigation in Airborne Passive Radars with Contaminated Reference Signals

For an airborne passive radar with contaminated reference signals, the clutter caused by multipath (MP) signals involved in the reference channel (MP clutter) corrupts the covariance estimation in space-time adaptive processing (STAP). In order to overcome the severe STAP performance degradation caused by impure reference signals and off-grid effects, a novel MP clutter suppression method based on local search is proposed for airborne passive radar. In the proposed method, the global dictionary is constructed based on the sparse measurement model of MP clutter, and the global atoms that are most relevant to the residual are selected. Then, the local dictionary is designed iteratively, and local searches are performed to match real MP clutter points. Finally, the off-grid effects are mitigated, and the MP clutter is suppressed from all matched atoms. A range of simulations is conducted in order to demonstrate the effectiveness of the proposed method.

Identification of Dynamic Models by Using Metaheuristic Algorithms

A modified versions of metaheuristic algorithms are presented to compare their performance in identifying the structural dynamic systems. Genetic algorithm, biogeography based optimization algorithm, ant colony optimization algorithm and artificial bee colony algorithm are heuristic algorithms that have robustness and ease of implementation with simple structure. Different algorithms were selected some from evolution algorithms and other from swarm algorithms to boost the equilibrium of global searches and local searches, to compare the performance and investigate the applicability of proposed algorithms to system identification; three cases are suggested under different conditions concerning data availability, different noise rate and previous familiarity of parameters. Simulation results show these proposed algorithms produce excellent parameter estimation, even with little measurements and a high noise rate.

Benchmarking Optimisation Methods for Model Selection and Parameter Estimation of Nonlinear Systems

Characterisation and quantification of nonlinearities in the engineering structures include selecting and fitting a good mathematical model to a set of experimental vibration data with significant nonlinear features. These tasks involve solving an optimisation problem where it is difficult to choose a priori the best optimisation technique. This paper presents a systematic comparison of ten optimisation methods used to select the best nonlinear model and estimate its parameters through nonlinear system identification. The model selection framework fits the structure’s equation of motions using time-domain dynamic response data and takes into account couplings due to the presence of the nonlinearities. Three benchmark problems are used to evaluate the performance of two families of optimisation methods: (i) deterministic local searches and (ii) global optimisation metaheuristics. Furthermore, hybrid local–global optimisation methods are examined. All benchmark problems include a free play nonlinearity commonly found in mechanical structures. Multiple performance criteria are considered based on computational efficiency and robustness, that is, finding the best nonlinear model. Results show that hybrid methods, that is, the multi-start strategy with local gradient-based Levenberg–Marquardt method and the particle swarm with Levenberg–Marquardt method, lead to a successful selection of nonlinear models and an accurate estimation of their parameters within acceptable computational times.

Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems

The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main approaches can be grouped into the following categories: fast heuristics, metaheuristics, and local search. Fast heuristics and metaheuristics are used when pre-scheduling times are short and long, respectively. The third is commonly used when pre-scheduling time is limited by CPU seconds or by objective function evaluations. This paper focuses on optimizing the scheduling of parallel applications, considering the energy consumption during the idle time while no tasks are executing. Additionally, we detail a comparative literature study of the performance of lexicographic variants with local searches adapted to be stochastic and aware of idle energy consumption.