Development Of Computational Methods Research Articles

A Novel Crowding Genetic Algorithm and Its Applications to Manufacturing Robots

A niche genetic algorithm (GA) based on a novel twin-space crowding (TC) approach is proposed for solving multimodal manufacturing optimization problems. The proposed TC method is designed in a parameter-free paradigm. That is, when cooperatively exploring solutions with GAs, it does not require prior knowledge related to the solution space to design additional problem-dependent parameters in the evolutionary process. This feature makes the proposed TC method suitable for assisting GAs in solving real-world engineering optimization problems involving intractable solution landscapes. A set of numerical benchmark functions is used to compare effectiveness and efficiency in the proposed TCGA, in different niche GAs, and in several evolutionary computation methods. The TCGA is then used to solve multimodal joint-space inverse problems in serial-link robots to compare its convergence performance with that of conventional methods that apply the sharing function. Finally, the TCGA is used to solve iterative collision-free design problems for linkage-bar robotic hands to demonstrate its effectiveness for generating diverse solutions during the design process.

Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem

We introduce an experimentation procedure for evaluating and comparing optimization algorithms based on the Traveling Salesman Problem (TSP). We argue that end-of-run results alone do not give sufficient information about an algorithm's performance, so our approach analyzes the algorithm's progress over time. Comparisons of performance curves in diagrams can be formalized by comparing the areas under them. Algorithms can be ranked according to a performance metric. Rankings based on different metrics can then be aggregated into a global ranking, which provides a quick overview of the quality of algorithms in comparison. An open source software framework, the TSP Suite, applies this experimental procedure to the TSP. The framework can support researchers in implementing TSP solvers, unit testing them, and running experiments in a parallel and distributed fashion. It also has an evaluator component, which implements the proposed evaluation process and produces detailed reports. We test the approach by using the TSP Suite to benchmark several local search and evolutionary computation methods. This results in a large set of baseline data, which will be made available to the research community. Our experiments show that the tested pure global optimization algorithms are outperformed by local search, but the best results come from hybrid algorithms.

Coupled Data-Driven Evolutionary Algorithm for Toxic Cyanobacteria (Blue-Green Algae) Forecasting in Lake Kinneret

AbstractCyanobacteria blooming in surface waters have become a major concern worldwide, as they are unsightly, and cause a variety of toxins, undesirable tastes, and odors. Approaches of mathematical process-based (deterministic), statistically based, rule-based (heuristic), and artificial neural networks have been the subject of extensive research for cyanobacteria forecasting. This study suggests a new framework of linking an evolutionary computational method (a genetic algorithm) with a data driven modeling engine (model trees) for external loading, physical, chemical, and biological parameters selection, all coupled with their associated time lags as decision variables for cyanobacteria prediction in surface waters. The methodology is demonstrated through trial runs and sensitivity analyses on Lake Kinneret (the Sea of Galilee), Israel. Model trials produced good matching as depicted through the results correlation coefficient on verification data sets. Temperature was reconfirmed as a predominant par...

Voltage Swell Mitigation Using Flexible AC Transmission Systems Based on Evolutionary Computing Methods

The main subject of these paper deals with enhancing the steady-state and dynamics performance of the power grids by using new idea namely Advanced Flexible AC Transmission Systems based on Evolutionary Computing Methods. Control of the electric power system can be achieved by using the new trends as Particle Swarm Optimization applied to this subject to enhance the characteristics of controller performance. This paper studies and analyzes Advanced Flexible AC Transmission System to mitigate only one of power quality problems is voltage swell. The Advanced Flexible AC Transmission System, which will be used in this paper, is the most promising one, which known as Advanced Thyristor Controlled Series Reactors, and Advanced Static VAR Compensator were utilized in this research to mitigate the voltage swell aiming to reach. This paper focuses on the operation of the AFACTS device under turning off heavy load that may causes transformer damaged, as no research covers this problem by this technique. Particle Swarm Optimization is used to determine the value of series inductor connected to the Advanced Flexible AC Transmission System. The proposed algorithm formatting, deriving, coding and programming the network equations required to link AFACTS during steady-state and dynamic behaviors to the power systems tested on the IEEE 30 bus system as well as IEEE 14 bus system, and 9 bus system.

Multi-objective optimization of wire electrical discharge machining process using evolutionary computation method: Effect of cutting variation

This article focuses on comparing the performance of brass wire and zinc-coated brass wire that are widely used as the wire electrode in wire electrical discharge machining. To this end, an evolutionary computation method is presented based on non-dominated sorting genetic algorithm in order to find an optimization of rough cutting of the Ti-6Al-4V titanium alloy with the aid of response surface methodology modeling. This research examines the effects of three process parameters, namely, pulse on-time, pulse off-time, and peak current on the process outputs, that is, material removal rate, sparking gap, and white layer thickness. The obtained results indicated that zinc-coated wire was more predictable and it showed more reliable response in the experimental and modeling results. Additionally, the optimization results for both wires demonstrated the high performance of non-dominated sorting genetic algorithm approach to obtain the Pareto optimal set of solutions.

Multi-Objective Optimization of Bulk Vinyl Acetate Polymerization with Branching

Inclusion of long chain branching (LCB) in polymers is a challenging and important task in any free radical polymerization as LCB influences polymer product quality. In the present case, batch optimization study for the bulk polymerization of vinyl acetate has been considered to find optimal process conditions for imparting LCB in polymer architecture. A theoretical study has been conducted with a validated model to observe the effect of live radical concentration on LCB as this is an important factor for branching in polymer via chain transfer to polymer route. In order to obtain better polymer product in less time at various temperatures, a need was observed to perform a multi-objective optimization study as the selected objectives were conflicting in nature. Owing to the complex nature of moment-based species balance equations and molecular weight distribution function, elitist non-dominated sorting genetic algorithm II (NSGA II), a well-established multi-objective evolutionary algorithm, has been employed as an evolutionary computation method to find out the Pareto optimal solutions. Minimization of polymerization time, maximization of molecular weight and maximization of number average degree of branching (Bn) can be simultaneously achieved, while the solutions were obtained within the experimental range of polydispersity index and weight average molecular weight (Mw) given in the open literature. Results show a wide range of process choices satisfying process objectives and constraints, both in low as well as high temperature regions.

Shape Optimization of Airship Based on Constrained Particle Swarm Optimization

This paper presents an optimized process of the shape of airships using a Constrained Particle Swarm Optimization (CPSO) algorithm from a parametric multidisciplinary perspective. The optimizational model is considered from the multidisciplinary approach such as the pneumatic, the structure and the strength of the airship, rather than the single disciplinary previously. Moreover, CPSO is first proposed to optimize the airship shape by optimizing the positions of the slenderness ratio in the continuous space. CPSO, as an evolutionary computation method, improves Particle Swarm Optimization (PSO) and adds a constraint condition so that it has the characteristics of random starting, parallel search and strong memorability. Our method, proved by experiment, has the great advantages of the fast convergence speed, the less parameter Settings and the easy achievement etc. The proposed pipeline is tested on a real case study and the comprehensive experimental results are shown to demonstrate the effectiveness of the presented approach.

Modelling real earthquake activity with reverse engineering based on evolutionary computation methods

Hazard assessment of dangerous natural phenomena is critical because of their evident results concerning loss of human life and property, especially in dense populated areas. Earthquakes are probably the most devastating phenomenon since their immediate and long-term consequences are severe. This study is focused on the earthquake data analysis in different regions of Greece, characterised by different seismicity levels. In specific, a novel model is proposed based on evolutionary computation methods, such as symbolic regression by genetic programming and genetic algorithms in order to elucidate preliminary hidden mathematical relations and patterns found in the seismological signals under study. Furthermore, the model is calibrated using reverse engineering and closes the loop from the data collection to initial hypothesis. In this way, the model formation is achieved. The presented simulation results qualitatively and quantitatively reveal some of the fundamental characteristics of each studied geographical region located in Greece that stem from its geodynamic properties.

Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms

Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and dissimilarities. This paper establishes a clear and fundamental algorithmic linking between particle swarm optimization (PSO) algorithm and genetic algorithms (GAs). Specifically, we select the task of solving unimodal optimization problems, and demonstrate that key algorithmic features of an effective Generalized Generation Gap based Genetic Algorithm can be introduced into the PSO by leveraging this algorithmic linking while significantly enhance the PSO's performance. However, the goal of this paper is not to solve unimodal problems, neither is to demonstrate that the modified PSO algorithm resembles a GA, but to highlight the concept of algorithmic linking in an attempt towards designing efficient optimization algorithms. We intend to emphasize that the evolutionary and other optimization researchers should direct more efforts in establishing equivalence between different genetic, evolutionary and other nature-inspired or non-traditional algorithms. In addition to achieving performance gains, such an exercise shall deepen the understanding and scope of various operators from different paradigms in Evolutionary Computation (EC) and other optimization methods.

Using modified fruit fly optimisation algorithm to perform the function test and case studies

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models.

Evolutionary Computation and QSAR Research

The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.

An adaptive penalty based covariance matrix adaptation–evolution strategy

Although most of unconstrained optimization problems with moderate to high dimensions can be easily handled with Evolutionary Computation (EC) techniques, constraint optimization problems (COPs) with inequality and equality constraints are very hard to deal with. Despite the fact that only equality constraints can be used to eliminate a certain variable, both types of constraints implicitly enforce a relation between problem variables. Most conventional constraint handling methods in EC do not consider the correlations between problem variables imposed by the problem constraints. This paper relies on the idea that a proper genetic operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. With this in mind, we employ a (μ+λ)-Evolution Strategy with a simplified variant of Covariance Matrix Adaptation based mutation operator along an adaptive weight adjustment scheme. The proposed algorithm is tested on two test sets. The outperformance of the algorithm is significant on the first benchmark when compared with five conventional methods. The results on the second test set show that algorithm is highly competitive when benchmarked with three state-of-art algorithms. The main drawback of the algorithm is its slightly lower speed of convergence for problems with high dimension and/or large search domain.

Hybrid evolutionary computation methods for quay crane scheduling problems

Quay crane scheduling is one of the most important operations in seaport terminals. The effectiveness of this operation can directly influence the overall performance as well as the competitive advantages of the terminal. This paper develops a new priority-based schedule construction procedure to generate quay crane schedules. From this procedure, two new hybrid evolutionary computation methods based on genetic algorithm (GA) and genetic programming (GP) are developed. The key difference between the two methods is their representations which decide how priorities of tasks are determined. While GA employs a permutation representation to decide the priorities of tasks, GP represents its individuals as a priority function which is used to calculate the priorities of tasks. A local search heuristic is also proposed to improve the quality of solutions obtained by GA and GP. The proposed hybrid evolutionary computation methods are tested on a large set of benchmark instances and the computational results show that they are competitive and efficient as compared to the existing methods. Many new best known solutions for the benchmark instances are discovered by using these methods. In addition, the proposed methods also show their flexibility when applied to generate robust solutions for quay crane scheduling problems under uncertainty. The results show that the obtained robust solutions are better than those obtained from the deterministic inputs.

Evolutionary Computation Methods in Image Watermarking

Watermarking can be described as a desirable alternative solution for copyright protection of digital materials like images, video and audio. This paper introduced a modified watermarking method in Wavelet domain to get better performance. Particle Swarm Optimization (PSO) is used to find the best DWT coefficients in the HL sub - band for embedding the watermark sequence and PSO fitness function can be formed from the watermarked image with the best possible quality, and proved execution time.

THE ANALYSIS OF QUASI-OPTIMAL TOPOLOGIES OF NETWORKS-ON-CHIP ON MEETING THE GLOBAL OPTIMUM

The article considers the main approaches to the synthesis of networks-on-chip (NoC) on the basis of regular and specialized topologies; their advantages and disadvantages are characterized. The association of both approaches to the NoC synthesis based on the specialized and regular topologies by applying predefined optimal and quasi-optimal topologies is proposed; it makes possible to reduce the disadvantages of both approaches by combining their advantages. Therefore, the criteria of NoC topology optimality are defined. Besides, a new class of quasi-optimal topologies is proposed and also the requirements to quasi-optimal topologies are specified. The applications of Monte-Carlo method and evolutionary computation method makes it possible to synthesize quasi-optimal topologies with the number of nodes amounting the hundreds, but being in the local optimum, that does not guarantee the most optimal topologies finding. For the analysis of the obtained quasi-optimal topologies on their proximity to the theoretical optimum the approximated characteristics of theoretically possible optimal topologies for the given number of nodes and constraints by linear programming problem formulation and application of mathematical methods of optimization are found. The analysis of the obtained quasi-optimal topologies for the number of nodes of 25 shows that they have the efficiency characteristic, which is only 1,8–3,7% different from the efficiency characteristic of approximated optimal topologies. This demonstrates the high efficiency of the synthesized quasi-optimal topologies and the possibility of their application to the synthesis of NoCs.

A Survey on Optimization Algorithms for Optimizing the Numerical Functions

Till now many optimization algorithms are available for solving complex problems. The algorithm that has created an impact is the numerical function optimization. Some of the existing optimization algorithms are classified into evolutionary computing and metaheuristic methods. A comprehensive survey on numerical function optimization is presented below with a deeper view into different aspects of the algorithms.

Big Bang - Big Crunch Optimization Algorithm with Local Directional Moves

The big bang--big crunch (BB--BC) algorithm has been proposed as a novel optimization method that relies on one of the theories of the evolution of the universe, namely the big bang and big crunch theory. It has been shown that this algorithm is capable of quick convergence, even in long, narrow parabolic-shaped flat valleys or in the existence of several local extremes. In this work, the BB--BC optimization algorithm is hybridized with local search moves in between the ``banging'' and ``crunching'' phases of the algorithm. These 2 original phases of the BB--BC algorithm are preserved, but the representative point (``best'' or ``fittest'' point) attained after the crunching phase of the iteration is modified with some local directional moves, using the previous representative points so far attained, with the hope that a better representative point would be obtained. The effect of imposing local directional moves is inspected by comparing the results of the original and enhanced BB--BC algorithm on various benchmark test functions. Moreover, the crunching phase of the algorithm is improved with the addition of a simplex-based local search routine. The results of the new hybridized algorithm are compared with the state-of-the-art variants of widely used evolutionary computation methods, namely genetic algorithms, covariance matrix adaptation evolutionary strategies, and particle swarm optimization. The results over the benchmark test functions have proven that the BB--BC algorithm, enhanced with local directional moves, has provided more accuracy with the same computation time or for the same number of function evaluations.

Model-based Alignment using Evolutionary Computation and Particle Filter in a Non-messy Place

In this paper, we propose a model-based alignment system using evolutionary computation and particle filter for augmented reality (AR). AR is a technique to overlay useful virtual information on the real world. To realize the AR, alignment between real space and virtual space is a serious problem. In particular, an initial frame alignment for real-time tracking is needed in the prior knowledge based alignment. Therefore, we propose an alignment method using evolutionary computation for the initial frame alignment. In this paper, we use the multiple evolutionary computation methods for designing an effective method of alignment system. Moreover, we conduct tracking of the alignment target using particle filter based on the initial frame alignment results. By using this system, a model-based alignment can be conducted in a non-messy place such as being hard to acquire many feature points.

Large‐scale data mining using genetics‐based machine learning

AbstractIn the last decade, genetics‐based machine learning methods have shown their competence in large‐scale data mining tasks because of the scalability capacity that these techniques have demonstrated. This capacity goes beyond the innate massive parallelism of evolutionary computation methods by the proposal of a variety of mechanisms specifically tailored for machine learning tasks, including knowledge representations that exploit regularities in the datasets, hardware accelerations or data‐intensive computing methods, among others. This paper reviews different classes of methods that alone or (in many cases) combined accelerate genetics‐based machine learning methods. © 2013 Wiley Periodicals, Inc.This article is categorized under: Technologies > Classification Technologies > Computational Intelligence

A Novel Quantum-behaved Particle Swarm Optimization Algorithm and Its Application to Parameter Optimization of Fuzzy Neural Networks

A novel Quantum-behaved Particle Swarm Optimization Algorithm with comprehensive learning and cooperative learning approach (CCQPSO) was introduced to improve the global convergence property of QPSO. In the proposed algorithm, the updating method of local attractor, particle’s previous best position and swarm’s global best position were performed in each dimension of the solution vector to avoid loss some components that had moved closer to the global optimal solution in the vector. Then the novel algorithm was applied to parameter optimization of fuzzy neural networks. The introduction of cooperative learning strategy is the originality in the proposed method. The results of prediction of chaotic time series experiment show that the proposed technique can converge more rapidly than other evolutionary computation methods, and the novel method is effective and efficient.