Improvements to single-objective constrained predator–prey evolutionary optimization algorithm

In predator–prey algorithm, a relatively small number of predators (“lions”) and a much larger number of prey (“antelopes”) are randomly placed on a two dimensional lattice with connected ends representing an unfolded surface of a torus. The predators are partially or completely biased towards one or more objectives, based on which each predator kills the weakest prey in its neighborhood. A stronger prey created through evolution replaces this prey. In case of constrained problems, the sum of constraint violations serves as an additional objective. Modifications of the basic predator–prey algorithm have been implemented in this paper regarding the selection procedure, apparent movement of the predators, and mutation strategy. Further modifications have been made making the algorithm capable of handling multiple equality and inequality constraints. The final modified algorithm was tested on standard linear/nonlinear and constrained/unconstrained single-objective optimization problems.     

An improved firefly algorithm for numerical optimization problems and it’s application in constrained optimization

Engineering with Computers - Metaheuristic algorithms are successful methods of optimization. The firefly algorithm is one of the known metaheuristic algorithms used in a variety of applications....     

Gates towards evolutionary large-scale optimization: A software-oriented approach to genetic algorithms—I. General perspective

Genetic algorithms comprise a novel methodology that has proven to be powerful in approaching complex, large-scale optimization problems in a wide variety of sciences, recently including computational chemistry. However, as it turns out, up to now the exploitation of this power is not at all a straightforward matter for many potential practitioners, among which are computational chemists. Both parts of this paper provide keys to this group of scientists that should enable them to open gates towards genetic algorithm applications on computers. After a general introduction, Part I presents a taxonomy for genetic algorithm software. The Discussion highlights important properties of different kinds of genetic algorithm software, and proposes a strategy to the applied scientist who needs an executable application without first having to become an expert in genetic algorithm science. The material presented is largely based on our past experience, which includes the insights that we gained during the development and use of our software library GATES. Applications built with GATES are spread across various fields of computational chemistry. GATES is described in Part II, the accompanying paper.     

A modified fuzzy min–max neural network with rule extraction and its application to fault detection and classification

The fuzzy min–max (FMM) network is a supervised neural network classifier that forms hyperboxes for classification and prediction. In this paper, we propose modifications to FMM in an attempt to improve its classification performance when a small number of large hyperboxes are formed in the network. Given a new input pattern, in addition to measuring the fuzzy membership function of the input pattern to the hyperboxes formed in FMM, an Euclidean distance measure is introduced for predicting the target class associated with the new input pattern. A rule extraction algorithm is also embedded into the modified FMM network. A confidence factor is calculated for each FMM hyperbox, and a user-defined threshold is used to prune the hyperboxes with low confidence factors. Fuzzy if–then rules are then extracted from the pruned network. The benefits of the proposed modifications are twofold, viz., to improve the performance of FMM when large hyperboxes are formed in the network; to facilitate the extraction of a compact rule set from FMM to justify its predictions. To assess the effectiveness of modified FMM, two benchmark pattern classification problems are experimented, and the results from different methods published in the literature are compared. In addition, a fault detection and classification problem with a set of real sensor measurements collected from a power generation plant is evaluated using modified FMM. The results obtained are analyzed and explained, and implications of the modified FMM network as a useful fault detection and classification tool in real environments are discussed.     

A globally and quadratically convergent primal–dual augmented Lagrangian algorithm for equality constrained optimization

We present a primal–dual augmented Lagrangian method to solve an equality constrained minimization problem. This is a Newton-like method applied to a perturbation of the optimality system that follows from a reformulation of the initial problem by introducing an augmented Lagrangian function. An important aspect of this approach is that, by a choice of suitable updating rules of parameters, the algorithm reduces to a regularized Newton method applied to a sequence of optimality systems. The global convergence is proved under mild assumptions. An asymptotic analysis is also presented and quadratic convergence is proved under standard regularity assumptions. Some numerical results show that the method is very efficient and robust.     

A multi-objective cost–benefit optimization algorithm for network hardening

International Journal of Information Security - Network hardening is an optimization problem to find the best combination of countermeasures to protect a network from cyber-attacks. While an...     

A differential evolution approach for solving constrained min–max optimization problems

Many problems occurring in engineering can be formulated as min–max optimization problems, for instance, in game theory, robust optimal control and many others. Min–max problems are considered difficult to solve, specially constrained min–max optimization problems. Approaches using co-evolutionary algorithms have successfully been used to solve min–max optimization problems without constraints. We propose a novel differential evolution approach consisting of three populations with a scheme of copying individuals for solving constrained min–max problems. Promising results have been obtained showing the suitability of the approach.     

Heuristic and exact algorithms for a min–max selective vehicle routing problem

In this work, we investigate a vehicle routing problem where not all clients need to be visited and the goal is to minimize the longest vehicle route. We propose two exact solution approaches for solving the problem: a Branch-and-cut (BC) algorithm and a Local Branching (LB) method that uses BC as its inner solver. Our computational experience indicates that, in practice, the problem is difficult to solve, mainly when the number of vehicles grows. In addition to the exact methods, we present a heuristic that relies on GRASP and on the resolution of a restricted integer program based on a set covering reformulation for the problem. The heuristic was capable of significantly improving the best solutions provided by BC and LB, in one tenth of the times taken by them to achieve their best upper bounds.     

Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms

Neural Computing and Applications - Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for...     

The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms

This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean–variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2), and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets.     

Gait modification and optimization using neural network–genetic algorithm approach: Application to knee rehabilitation

Gait modification strategies play an important role in the overall success of total knee arthroplasty. There are a number of studies based on multi-body dynamic (MBD) analysis that have minimized knee adduction moment to offload knee joint. Reducing the knee adduction moment, without consideration of the actual contact pressure, has its own limitations. Moreover, MBD-based framework that mainly relies on iterative trial-and-error analysis, is fairly time consuming. This study embedded a time-delay neural network (TDNN) in a genetic algorithm (GA) as a cost effective computational framework to minimize contact pressure. Multi-body dynamic and finite element analyses were performed to calculate gait kinematics/kinetics and the resultant contact pressure for a number of experimental gait trials. A TDNN was trained to learn the nonlinear relation between gait parameters (inputs) and contact pressures (output). The trained network was then served as a real-time cost function in a GA-based global optimization to calculate contact pressure associated with each potential gait pattern. Two optimization problems were solved: first, knee flexion angle was bounded within the normal patterns and second, knee flexion angle was allowed to be increased beyond the normal walking. Designed gait patterns were evaluated through multi-body dynamic and finite element analyses.The TDNN-GA resulted in realistic gait patterns, compared to literature, which could effectively reduce contact pressure at the medial tibiofemoral knee joint. The first optimized gait pattern reduced the knee contact pressure by up to 21% through modifying the adjacent joint kinematics whilst knee flexion was preserved within normal walking. The second optimized gait pattern achieved a more effective pressure reduction (25%) through a slight increase in the knee flexion at the cost of considerable increase in the ankle joint forces. The proposed approach is a cost-effective computational technique that can be used to design a variety of rehabilitation strategies for different joint replacement with multiple objectives.     

Artificial neural network application on microstructure–compressive strength relationship of cement mortar

Artificial neural network analysis was performed to establish a relationship between microstructural characteristics and compressive strength values of cement mortar in this study. Pore properties such as pore area ratio, total pore length, total dendrite length and average roundness, and paste properties such as hydrated part area and unhydrated part area ratios were approached as microstructural characteristics obtained by digital image analysis. These microstructural quantities were correlated with compressive strength values of cement mortar incorporating with the chemical admixtures by different dosages, which resulted as several microstructural characteristics. Artificial neural network (ANN) analysis indicated that by using ANN as non-linear statistical data modeling tool, a strong correlation between the microstructural properties of cement mortar and compressive strengths can be established.     

Whom to talk to? A stakeholder perspective on business process development

Although many organizations operate in a process-driven way, few members are skilled in specifying and developing business processes??a skill that has become crucial for organization development, in particular to establish agile enterprises. This paper shows, on the basis of natural language constructs (subject, predicate, object) and communication patterns between actors (subjects), how individual members of an organization could contribute to coherent and intelligible process specifications. A language and tool supporting Subject-oriented Business Process Management (S-BPM) are introduced, allowing organizations to cope with strategic and operational challenges dynamically. As many organizations already work with BPM concepts and technologies, existing approaches to process modelling are also revisited with respect to representing natural language constructs and standard sentence syntax. Since most of them refer either to subjects, predicates, objects or to a respective combination, a roadmap can be developed for enriching existing modelling approaches. In doing so, organizations can benefit from stakeholder inputs for effective business process engineering re-using existing specifications.     

Issues on computers and development—A Kenya perspective

The issues pertaining to computers and development are at best complex. This paper examines the implications of manpower, education and training, IT policy, job replacement, culture and traditions, consultancy and marketing, IT isolationism in the context of Kenya.     

Classification and counting on multi-continued fractions and its application to multi-sequences

In the light of multi-continued fraction theories, we make a classification and counting for multi-strict continued fractions, which are corresponding to multi-sequences of multiplicity m and length n. Based on the above counting, we develop an iterative formula for computing fast the linear complexity distribution of multi-sequences. As an application, we obtain the linear complexity distributions and expectations of multi-sequences of any given length n and multiplicity m less than 12 by a personal computer. But only results of m=3 and 4 are given in this paper.     

The optimization landscape of hybrid quantum–classical algorithms: From quantum control to NISQ applications

This review investigates the landscapes of hybrid quantum–classical optimization algorithms that are prevalent in many rapidly developing quantum technologies, where the objective function is computed by either a natural quantum system or an engineered quantum ansatz, but the optimizer is classical. In any particular case, the nature of the underlying control landscape is fundamentally important for systematic optimization of the objective. In early studies on the optimal control of few-body dynamics, the optimizer could take full control of the relatively low-dimensional quantum systems to be manipulated. Stepping into the noisy intermediate-scale quantum (NISQ) era, the experimentally growing computational power of the ansatz expressed as quantum hardware may bring quantum advantage over classical computers, but the classical optimizer is often limited by the available control resources. Across these different scales, we will show that the landscape’s geometry experiences morphological changes from favorable trap-free landscapes to easily trapping rugged landscapes, and eventually to barren-plateau landscapes on which the optimizer can hardly move. This unified view provides the basis for understanding classes of systems that may be readily controlled out to those with special consideration, including the difficulties and potential advantages of NISQ technologies, as well as seeking possible ways to escape traps or plateaus, in particular circumstances.     

Heuristic and exact algorithms for the max–min optimization of the multi-scenario knapsack problem

We are concerned with a variation of the standard 0–1 knapsack problem, where the values of items differ under possible S scenarios. By applying the ‘pegging test’ the ordinary knapsack problem can be reduced, often significantly, in size; but this is not directly applicable to our problem. We introduce a kind of surrogate relaxation to derive upper and lower bounds quickly, and show that, with this preprocessing, the similar pegging test can be applied to our problem. The reduced problem can be solved to optimality by the branch-and-bound algorithm. Here, we make use of the surrogate variables to evaluate the upper bound at each branch-and-bound node very quickly by solving a continuous knapsack problem. Through numerical experiments we show that the developed method finds upper and lower bounds of very high accuracy in a few seconds, and solves larger instances to optimality faster than the previously published algorithms.     

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,     

A short note on applying a simple LS/LNS-based metaheuristic to the rollon–rolloff vehicle routing problem

The rollon–rolloff vehicle routing problem (RRVRP) arises when tractors move containers or trailers between locations generating a high volume of waste like construction sites, etc., and a disposal facility. It can be formulated as a node routing problem with asymmetric arc cost and a maximum route length. In this paper we show that a simple and flexible approach which combines standard local search and large neighborhood search moves under two parameter-free/-poor metaheuristic controls outperforms special purpose developments published in the literature. Our results are obtained by customizing a RRVRP-solver from a framework developed for solving rich vehicle routing problem variants.