Artificial Intelligence for Automatic Container Stowage Planning Optimisation

Geophysical Inverse Theory and Regularization Problems

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Fortran programs for the time-dependent Gross–Pitaevskii equation in a fully anisotropic trap

This paper presents a method of constructing accurate nonlinear dynamic MIMO models using a class of fuzzy logic systems (FLSs). Generally, the design of FLS models involves identification of both the FLS structure (linguistic fuzzy IF-THEN rules) and parameter tuning of fuzzy membership functions. Most current techniques treat these parts separately, which may result in a suboptimal solution. The authors propose to optimize the two parts simultaneously using a genetic algorithm (GA), a stochastic global search method which explores the solution space in manner similar to natural evolution. In the authors' work new FLS domain specific operators are introduced that significantly reduce search time, and at the same time, increase the accuracy of results. The FLS model obtained from GA search is further fine-tuned using a conjugate gradient method. To illustrate the proposed method, the authors show that the FLS models compare well to other nonparametric modeling techniques, such as standard feedforward neural networks on an example of a nonlinear system. In addition, a FLS offers qualitative model description in terms of the linguistic fuzzy rules which may contribute to the understanding and control of the process. These merits make the FLS models attractive for use in model predictive control and other process applications.

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.

One of the main problems in modern mathematical modeling is to obtain high-precision solutions of boundary value problems. This study proposes a new approach that combines the methods of artificial intelligence and a classical analytical method. The use of the analytical method of fictitious canonic regions is proposed as the basis for obtaining reliable solutions of boundary value problems. The novelty of the approach is in the application of artificial intelligence methods, namely, genetic algorithms, to select the optimal location of fictitious canonic regions, ensuring maximum accuracy. A general genetic algorithm has been developed to solve the problem of determining the global minimum for the choice and location of fictitious canonic regions. For this genetic algorithm, several variants of the function of crossing individuals and mutations are proposed. The approach is applied to solve two test boundary value problems: the stationary heat conduction problem and the elasticity theory problem. The results of solving problems showed the effectiveness of the proposed approach. It took no more than a hundred generations to achieve high precision solutions in the work of the genetic algorithm. Moreover, the error in solving the stationary heat conduction problem was so insignificant that this solution can be considered as precise. Thus, the study showed that the proposed approach, combining the analytical method of fictitious canonic regions and the use of genetic optimization algorithms, allows solving complex boundary-value problems with high accuracy. This approach can be used in mathematical modeling of structures for responsible purposes, where the accuracy and reliability of the results is the main criterion for evaluating the solution. Further development of this approach will make it possible to solve with high accuracy of more complicated 3D problems, as well as problems of other types, for example, thermal elasticity, which are of great importance in the design of engineering structures.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

In a timetabling problem, exam subjects must be slotted to certain times that satisfy several of constraints. They are NP-completeness problems, which usually lead to satisfactory but sub-optimal solutions. This paper investigates and compares Genetic Algorithm and Tabu Search approaches to solve these kinds of problem. The experiment shows that TS approach can produce better timetables than those of GA approach can. Further, the search time spent in TS is less than that of GA. However, GA can produce several different near optimal solutions simultaneously.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

We describe a generic approach to image interpretation, based on combining a general method of building flexible template models with Genetic Algorithm (GA) search. The method can be applied to a given image interpretation problem simply by training a Point Distribution Model (PDM), using a set of examples of the image structure to be located. A local optimisation technique, developed for use with PDMs, has been incorporated into the GA search with the aim of improving the speed of convergence and optimality of solution. We present results, from three practical applications, demonstrating that the new method offers significant improvements when compared to previously reported approaches to flexible template matching. The benefits include the ability to deal with different domains of application using a standard method, the ability to deal with complex multi-part models and improved search performance.

The scheduling problem is a typical time table problem in educational administration. For such a NP complete problems, when the genetic algorithm solves this problem, it has precociousness phenomenon and quickly converges not to the global optimal solution but to the local optimal solution. Therefore, we use the advantage of local search method and partial matching crossover operation to solve TTP problem with the genetic algorithm combining search algorithm. We do a lot of experiments and evaluate the performance of the improved genetic algorithm. The experiment results show that genetic search algorithm is a more superior algorithm to apply to the TTP problem.

The performance of two new heuristics that combine tabu search with a genetic crossover technique was examined for their usefulness in solving spatially constrained harvest scheduling problems. One heuristic utilized tabu search with 1-opt moves and a genetic crossover technique (TS/GA), and the other utilized tabu search with both 1-opt and 2-opt moves and a genetic crossover technique (TS2/GA). The heuristics were tested on four problems. Three problems used the simple unit-restriction model (URM) to portray greenup constraints, allowing them to be solved with mathematical programming techniques and thus providing a benchmark to compare against the solutions produced by the heuristic techniques. These were considered hypothetical problems, since the datasets were grids, and the age classes were assigned with three different rules. The fourth problem uses an operational dataset and includes both a maximum opening size constraint, which limits the size of an individual opening, and a maximum average opening size constraint, which represents the greenup constraint contained in the American Forest and Paper Association (AF&PA) Sustainable Forestry Initiative (AF&PA 2000). The greenup constraints contained in these problems accurately portray the greenup constraints facing the forest industry in the United States. For the three hypothetical problems, the TS/GA technique found solutions with an objective function value between 96.6% and 99.1% of an estimated optimal value, and between 93.4% to 94.4% of a relaxed linear programming value. The TS2/GA found better solutions for all three hypothetical problems, with the objective function values between 98.2% and 99.7% of the estimated optimal value and the 96.3% to 97.2% of the relaxed linear programming value. Compared with a general tabu search technique that used 1-opt moves (TS), adding the genetic crossover technique resulted in a 2% increase in the objective function value, and adding the 2-opt intensification capability resulted in a further 1.5% improvement. A similar pattern was observed when the problem using the operational dataset was solved. The addition of the genetic crossover technique did not increase the time required to produce a solution to any particular problem, yet the addition of the 2-opt procedure did. TS2/GA required about twice as much time as did TS or TS/GA when applied to the three hypothetical datasets, and 67% more time when applied to the problem using the operational dataset. FOR. SCI. 48(1):35–46.

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.

The chapter covers two main areas, these being an introduction to the technology and techniques associated with genetic algorithms and then the second part looks at how genetic algorithms can be used to search for good topological solutions to engineering design challenges. The start of the chapter places genetic algorithms in context compared to other evolutionary algorithms and also describes the reasons why genetic algorithms are potentially useful. This is then followed by a look at the concept of a search space. Section two looks at the canonical genetic algorithm as a basic introduction to the technology and includes an examination of the main techniques used to encode the genome, fitness functions, operators and selection. Section three looks at how genetic algorithms can be used for design and chooses the specific example of the conceptual design of commercial office buildings. Section four introduces the basic concepts of topological search and explains how having the right form of representation is vital before looking at example relating to structural components and the design of domes using a genetic algorithm linked to computational geometry techniques. The final section then looks at further methods using generative representations and generative geometries as possible solutions to the need to develop powerful forms of representation for handling topological search in genetic algorithms.

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