Optimization of Allelic Combinations Controlling Parameters of a Peach Quality Model

Process-based models are effective tools to predict the phenotype of an individual in different growing conditions. Combined with a quantitative trait locus (QTL) mapping approach, it is then possible to predict the behavior of individuals with any combinations of alleles. However the number of simulations to explore the realm of possibilities may become infinite. Therefore, the use of an efficient optimization algorithm to intelligently explore the search space becomes imperative. The optimization algorithm has to solve a multi-objective problem, since the phenotypes of interest are usually a complex of traits, to identify the individuals with best tradeoffs between those traits. In this study we proposed to unroll such a combined approach in the case of peach fruit quality described through three targeted traits, using a process-based model with seven parameters controlled by QTL. We compared a current approach based on the optimization of the values of the parameters with a more evolved way to proceed which consists in the direct optimization of the alleles controlling the parameters. The optimization algorithm has been adapted to deal with both continuous and combinatorial problems. We compared the spaces of parameters obtained with different tactics and the phenotype of the individuals resulting from random simulations and optimization in these spaces. The use of a genetic model enabled the restriction of the dimension of the parameter space toward more feasible combinations of parameter values, reproducing relationships between parameters as observed in a real progeny. The results of this study demonstrated the potential of such an approach to refine the solutions toward more realistic ideotypes. Perspectives of improvement are discussed.

Applications of Harmony Search Algorithms in Engineering

In a recent article the authors reviewed the principles of harmony search and journal articles on harmony search algorithm (HSA). This article presents a review of applications of HSA including structural design, hydrologic model design, water distribution network design, water pump switching problem, transmission network expansion planning problem, job shop scheduling problem, university timetable and rosterering problem, training neural networks, clustering, combined heat and power economic dispatch problem, economic load dispatch problem, and economic and emission dispatch problem.

A Swarm Optimization approach for clinical knowledge mining

BACKGROUND AND OBJECTIVES

Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm.

METHODS

WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization.

RESULTS

Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively.

CONCLUSION

WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy.

Parameter optimization method for the water quality dynamic model based on data-driven theory

Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB).

Lycurgus Cup: inverse problem using photographs for characterization of matter

Photographs of the Lycurgus Cup with a source light inside and outside exhibit purple and green colors, respectively (dichroism). A model relying on the scattering of light to colors in the photographs is proposed and used within an inverse problem algorithm, to deduce radius and composition of metallic particles, and the refractive index of the surrounding glass medium. The inverse problem algorithm is based on a hybridization of particle swarm optimization and of the simulated annealing methods. The results are compared to experimental measurements on a small sample of glass. The linear laws that are deduced from sets of possible parameters producing the same color in the photographs help simplify the understanding of phenomena. The proportion of silver to gold in nanoparticles is found to be in agreement, but a large proportion of copper is also found. The retrieved refractive index of the surrounding glass is close to 2.

Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques

The complexity of helicopter flight dynamics makes modeling and helicopter system identification a very difficult task. Most of the traditional techniques require a model structure to be defined a priori and in case of helicopter dynamics, this is difficult due to its complexity and the interplay between various subsystems. To overcome this difficulty, non-parametric approaches are commonly adopted for helicopter system identification. Artificial Neural Network are a widely used class of algorithms for non-parametric system identification, among them, the Nonlinear Auto Regressive eXogeneous input network (NARX) model is very popular, but it also necessitates some in-depth knowledge regarding the system being modelled. There have been many approaches proposed to circumvent this and yet still retain the advantageous characteristics. In this paper, the authors carry out an extensive study of one such newly proposed approach - using a modified NARX model with a II-tiered, externally driven recurrent neural network architecture. This is coupled with an outer optimization routine for evolving the order of the system. This generic architecture is comprehensively explored to ascertain its usability and critically asses its potential. Different implementations of this architecture, based on nature inspired techniques, namely, Artificial Bee Colony (ABC), Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) are evaluated and critically compared in this paper. Simulations have been carried out for identifying the longitudinally uncoupled dynamics. Results of identification indicate a quite close correlation between the actual and the predicted response of the helicopter for all the models.

The Dawning of Computational Psychoanalysis

In this paper, the author wishes first to highlight, within the general cultural context, some possible elementary computational psychoanalysis formalizations concerning Matte Blanco's bi-logic components through certain very elementary mathematical tools and notions drawn from theoretical physics and algebra. Afterwards, on the basis of recent work of Giampaolo Sasso (1999; 2005; 2011), relying on the crucial crossroad between neurosciences and psychoanalysis, it will be possible to identify some hints for further formalization attempts turned toward a computational psychoanalysis outlook. Lastly, possible interesting relationships with cognitive informatics are also outlined.

A Non-Permutation Flow Shop Manufacturing Cell Scheduling Problem with Part's Sequence Dependent Family Setup Times

This article presents a new mathematical model for a dynamic flow shop manufacturing cell scheduling problem (DFMCSP) with agreeable job release date for each part family where family setup times are dependent on sequence of parts within families. It means this article considers non-permutation schedules for both sequence of families and sequence of parts within families. The objective is minimizing the Makespan (Cmax). Since, this problem belongs to NP-Hard class. Therefore, reaching an optimal solution in a reasonable computational time by using exact methods is extremely difficult. Thus, this article proposes meta-heuristic methods such as Genetic Algorithms (GA) and Tabu Search (TS). Finally, the computational results compare efficiency of the proposed algorithms under the performance measures.

Leafcutter Ant Colony Optimization Algorithm for Feature Subset Selection on Classifying Digital Mammograms

Ant Colony Optimization (ACO) has been applied in wide range of applications. In ACO, for every iteration the entire problem space is considered for the solution construction using the probability of the pheromone deposits. After convergence, the global solution is made with the path which has highest pheromone deposit. In this paper, a novel solution construction technique has been proposed to reduce the time complexity and to improve the performance of the ACO. The idea is derived from the behavior of a special ant species called ‘Leafcutter Ants', they spend much of their time for cutting leaves to make fertilizer to gardens in which they grow the fungi that they eat. This behavior is incorporated with the general ACO algorithm to propose a novel feature selection method called ‘Leafcutter Ant Colony Optimization' (LACO) algorithm. The LACO has been applied to select the relevant features for digital mammograms and their corresponding classification performance is studied and compared.

A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System

In this investigation, a novel fuzzy mathematical program based on thermodynamic principles is implemented to capture the uncertainties of a practical power system, known as Damavand power plant. The proposed intelligent machine takes the advantages of a niching bio-inspired learning mechanism to be reconciled to the requirements of the problem at hand. The aim of the bio-inspired fuzzy based intelligent system is to yield a model capable of recognizing different operating parameters of Damavand power system under different operating conditions. To justify the privileges of using a niching metaheuristic over gradient descend methods, the authors use the data, derived through data acquisition, together with a machine learning based approach to estimate the multi-modality associated with the training of the proposed fuzzy model. Moreover, the niching bio-inspired metaheuristic, niching particle swarm optimization (NPSO), is compared to canonical PSO (CPSO), stochastic social PSO (SSPSO), unified PSO (UPSO), comprehensive learning PSO (CLPSO), PSO with constriction factor (PSOCF) and fully informed PSO (FIPSO). Through experiments and analysis of the characteristics of the problem being optimized, it is proved that NPSO is not only able to tackle the deficiencies of the learning process, but also can effectively adjust the fuzzy approach to conduct the identification process with a high degree of robustness and accuracy.

Impacts of Scheduling Decisions Based On PSO Algorithm and Dispatching Rules on FMS Performances

This paper investigates the scheduling problem in a typical flexible manufacturing system (FMS) with routing flexibility. The scheduling decisions have been established in terms of how the parts are launched into the system, how these parts are routed through various machines and how parts are sequenced for processing on a machine. The first decision is taken using one of the two dynamic dispatching rules (least work remaining (LWR) and most work remaining (MWR)) or first in first out (FIFO). Concerning the second decision, an algorithm based on particle swarm optimization (PSO) is used for real time alternative routing selection of each part whereas shortest processing time (SPT), longest processing time (LPT) and FIFO are used as sequencing rules for working centres (third decision). The simulation results evaluated in terms of the productivity and the utilisation of the system show that the proposed PSO algorithm performs the best when it is combined with LWR as part launching rule and SPT as machine sequencing rule.

An Improved Ant-IS Algorithm for Intrusion Detection

During recent years, the number of attacks on networks has dramatically increased. Consequently the interest in network intrusion detection has increased among the researchers. This paper proposes a clustering Ant-IS and an active Ant colony optimization algorithms for intrusion detection in computer networks. The goal of these algorithms is to extract a set of learning instances from the initial training dataset. The proposed algorithms are an improvement of the previously presented Ant-IS algorithm, used is pattern recognition. Results of experimental tests show that the proposed algorithms are capable of producing a reliable intrusion detection system.

A classification scheme for agent based approaches to dynamic optimization

Several papers in the literature employ agent-based modeling approach for providing reasonable solutions to dynamic optimization problems (DOPs). However, these studies employ a variety of agent-based modeling approaches with different strategies and features for different DOPs. On the other hand, there is an absence in the literature of a formal representation of the existing agent-based solution strategies. This paper proposes a representation scheme indicating how the solution strategies with agent-based approach can be summarized in a concise manner. We present these in a tabular form called “Agent Based Dynamic Optimization Problem Solution Strategy” (ABDOPSS). ABDOPSS distinguishes different classes of agent based algorithms (via communication type, cooperation type, dynamism domain and etc.) by specifying the fundamental ingredients of each of these approaches with respect to problem domain (problems with dynamic objective functions, constraints and etc.). This paper also analyzes 18 generic studies in the literature employing agent-based modeling based on ABDOPSS.

AntMeshNet

Wireless Mesh Networks (WMNs) are emerging as evolutionary self organizing networks to provide connectivity to end users. Efficient Routing in WMNs is a highly challenging problem due to existence of stochastically changing network environments. Routing strategies must be dynamically adaptive and evolve in a decentralized, self organizing and fault tolerant way to meet the needs of this changing environment inherent in WMNs. Conventional routing paradigms establishing exact shortest path between a source-terminal node pair perform poorly under the constraints imposed by dynamic network conditions. In this paper, the authors propose an optimal routing approach inspired by the foraging behavior of ants to maximize the network performance while optimizing the network resource utilization. The proposed AntMeshNet algorithm is based upon Ant Colony Optimization (ACO) algorithm; exploiting the foraging behavior of simple biological ants. The paper proposes an Integrated Link Cost (ILC) measure used as link distance between two adjacent nodes. ILC takes into account throughput, delay, jitter of the link and residual energy of the node. Since the relationship between input and output parameters is highly non-linear, fuzzy logic was used to evaluate ILC based upon four inputs. This fuzzy system consists of 81 rules. Routing tables are continuously updated after a predefined interval or after a change in network architecture is detected. This takes care of dynamic environment of WMNs. A large number of trials were conducted for each model. The results have been compared with Adhoc On-demand Distance Vector (AODV) algorithm. The results are found to be far superior to those obtained by AODV algorithm for the same WMN.

A State-of-the-Art Review of Artificial Bee Colony in the Optimization of Single and Multiple Criteria

Nowadays computers are used to solve a variety and multitude of complex problems facing in every sphere of peoples’ life. However, many of the problems are intractable in nature exact algorithm might need centuries to manage with formidable challenges. In such cases heuristic or in a broader sense meta-heuristic algorithms that find an approximate solution but have acceptable time and space complexity play indispensable role. In this article, the authors present a state-of-the-art review on meta-heuristic algorithm popularly known as artificial bee colony (ABC) inspired by honey bees. Moreover, the ABC algorithm for solving single and multi-objective optimization problems have been studied. A few potential application areas of ABC are highlighted as an end note of this article.

An Efficient Ant Colony Instance Selection Algorithm for KNN Classification

The extraordinary progress in the computer sciences field has made Nearest Neighbor techniques, once considered impractical from a standpoint of computation (Dasarathy et al., 2003), became feasible for real-world applications. In order to build an efficient nearest neighbor classifier two principal objectives have to be reached: 1) achieve a high accuracy rate; and 2) minimize the set of instances to make the classifier scalable even with large datasets. These objectives are not independent. This work addresses the issue of minimizing the computational resource requirements of the KNN technique, while preserving high classification accuracy. This paper investigates a new Instance Selection method based on Ant Colonies Optimization principles, called Ant Instance Selection (Ant-IS) algorithm. The authors have proposed in a previous work (Miloud-Aouidate & Baba-Ali, 2012a) to use Ant Colony Optimization for preprocessing data for Instance Selection. However to the best of the authors’ knowledge, Ant Metaheuristic has not been used in the past for directly addressing Instance Selection problem. The results of the conducted experiments on several well known data sets are presented and compared to those obtained using a number of well known algorithms, and most known classification techniques. The results provide evidence that: (1) Ant-IS is competitive with the well-known kNN algorithms; (2) The condensed sets computed by Ant-IS offers also better classification accuracy then those obtained by the compared algorithms.

Hybridizing Harmony Search Algorithm with Multi-Parent Crossover to Solve Real World Optimization Problems

Harmony search algorithm (HSA) is a recent evolutionary algorithm used to solve several optimization problems. The algorithm mimics the improvisation behaviour of a group of musicians to find a good harmony. Several variations of HSA have been proposed to enhance its performance. In this paper, a new variation of HSA that uses multi-parent crossover is proposed (HSA-MPC). In this technique three harmonies are used to generate three new harmonies that will replace the worst three solution vectors in the harmony memory (HM). The algorithm has been applied to solve a set of eight real world numerical optimization problems (1-8) introduced for IEEE-CEC2011 evolutionary algorithm competition. The experimental results of the proposed algorithm are compared with the original HSA, and two variations of HSA: global best HSA and tournament HSA. The HSA-MPC almost always shows superiority on all test problems.

GeneticTKM

The clustering problem under the criterion of minimum sum square of errors is a non-convex and non-linear problem, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal solution. In this paper, a hybrid genetic, tabu search and k-means algorithm, called GeneticTKM, is proposed for the clustering problem. A new mutation operator is presented based on tabu search algorithm for the proposed hybrid genetic method. The key idea of the new operator is to produce tabu space for escaping from trap of local optimal and finding better solution. The results of the proposed algorithm are compared with other clustering algorithms such as genetic algorithm; tabu search and particle swarm optimization by implementing them and using standard and simulated data sets. The authors also compare the results of the proposed algorithm with other researchers’ results in clustering the standard data sets. The results show that the proposed algorithm can be considered as an effective and efficient algorithm to find better solution for the clustering problem.

Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification

This work presents a novel approach for classification of both balanced and unbalanced dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact it size. In this paper, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

The Strict Strong Coloring Based Graph Distribution Algorithm

State space explosion is a fundamental obstacle in formal verification of concurrent systems. As a solution for this problem, this paper deals with distributed state space. The authors’ solution is to introduce the coloring concept and dominance relation in graphs for finding the good distribution of given graphs. This basic solution is improved in two steps: the initialization and optimization step. The authors also report on a thorough experimental study to evaluate the performance of this new algorithm which depends strongly on the size, nature of the graphs, and the chosen number of workers. In addition, the quality of this algorithm is illustrated by comparison with the hash function (MD5) based algorithm. To the best of the authors’ knowledge, it is the first time when coloring concept is used to solve this problem.

SODAC

It is of common use that analog designers start by optimizing the basic building bloc forming an active circuit in order to ‘optimally’ size the latter. Even though it is known a priori that the overall circuit performances will differ from the expected ones, due to the fact that the performances of the basic cells will (considerably) change because of the surrounding circuitry, such approach is very widely used. This is mainly due to the complexity of these ‘complex’ circuits. It has recently been shown that the simulation based sizing technique is a very interesting spare solution, since it allows avoiding the (very) ‘complex’ modeling task. In this paper the authors propose a simulation based optimizing tool that can handle both mono-objective and multi-objective optimization sizing problems. Viability and benefits of this tool are highlighted through some examples. Obtained results are compared to the ideal expected ones and to the ones that are obtained using the conventional approaches.

Round-Table Group Optimization for Sequencing Problems

In this paper, a round-table group optimization (RTGO) algorithm is presented. RTGO is a simple meta-heuristic framework using the insights of research on group creativity. In a cooperative group, the agents work in iterative sessions to search innovative ideas in a common problem landscape. Each agent has one base idea stored in its individual memory, and one social idea fed by a round-table group support mechanism in each session. The idea combination and improvement processes are respectively realized by using a recombination search (XS) strategy and a local search (LS) strategy, to build on the base and social ideas. RTGO is then implemented for solving two difficult sequencing problems, i.e., the flowshop scheduling problem and the quadratic assignment problem. The domain-specific LS strategies are adopted from existing algorithms, whereas a general XS class, called socially biased combination (SBX), is realized in a modular form. The performance of RTGO is then evaluated on commonly-used benchmark datasets. Good performance on different problems can be achieved by RTGO using appropriate SBX operators. Furthermore, RTGO is able to outperform some existing methods, including methods using the same LS strategies.

Numerical retrieval of thin aluminium layer properties from SPR experimental data

The inverse problem for Surface Plasmon Resonance measurements [1] on a thin layer of aluminium in the Kretschmann configuration, is solved with a Particle Swarm Optimization method. The optical indexes as well as the geometrical parameters are found for the best fit of the experimental reflection coefficient in s and p polarization, for four samples, under three theoretical hypothesis on materials: the metal layer is pure, melted with its oxyde, or coated with oxyde. The influence of the thickness of the metal layer on its optical properties is then investigated.

A DIMMA-Based Memetic Algorithm for 0-1 Multidimensional Knapsack Problem Using DOE Approach for Parameter Tuning

Multidimensional 0-1 Knapsack Problem (MKP) is a well-known integer programming problems. The objective of MKP is to find a subset of items with maximum value satisfying the capacity constraints. A Memetic algorithm on the basis of Design and Implementation Methodology for Metaheuristic Algorithms (DIMMA) is proposed to solve MKP. DIMMA is a new methodology to develop a metaheuristic algorithm. The Memetic algorithm is categorized as metaheuristics and is a particular class of evolutionary algorithms. The parameters of the proposed algorithm are tuned by Design of Experiments (DOE) approach. DOE refers to the process of planning the experiment so that appropriate data that can be analyzed by statistical methods will be collected, resulting in valid and objective conclusions. The proposed algorithm is tested on several MKP standard instances from OR-Library. The results show the efficiency and effectiveness of the proposed algorithm.

Quantitative comparison of optimized nanorods, nanoshells and hollow nanospheres for photothermal therapy

The purpose of this study is to get more efficient gold nanoparticles, for necrosis of cancer cells, in photothermal therapy. Therefore a numerical maximization of the absorption efficiency of a set of nanoparticles (nanorod, nanoshell and hollow nanosphere) is proposed, assuming that all the absorbed light is converted to heat. Two therapeutic cases (shallow and deep cancer) are considered. The numerical tools used in this study are the full Mie theory, the discrete dipole approximation and the particle swarm optimization. The optimization leads to an improved efficiency of the nanoparticles compared with previous studies. For the shallow cancer therapy, the hollow nanosphere seems to be more efficient than the other nanoparticles, whereas the hollow nanosphere and nanorod, offer comparable absorption efficiencies, for deep cancer therapy. Finally, a study of tolerance for the size parameters to guarantee an absorption efficiency threshold is included.