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....     

Kendall’s correlation coefficient for vague preferences

The problem of measuring association between preference systems in situations with missing information or noncomparable outputs is discussed. New correlation coefficient, which generalizes Kendall’s correlation coefficients used traditionally in statistics, is suggested. The construction utilizes IF-sets.     

Saddle point criteria and Wolfe duality in nonsmooth (Φ, ρ)-invex vector optimization problems with inequality and equality constraints

In this paper, saddle point criteria and Wolfe duality theorems are established for a new class of nondifferentiable vector optimization problems with inequality and equality constraints. The results are proved under nondifferentiable (Φ, ρ)-invexity and related scalar and vector-valued Lagrangians defined for the considered nonsmooth multiobjective programming problem. It turns out that the results are established for such vector optimization problems in which not all functions constituting a vector optimization problem possess the fundamental property of invexity and the most of generalized invexity notions previously defined in the literature.     

Modeling agent’s preferences by its designer’s social value orientation

Human social preferences have been shown to play an important role in many areas of decision-making. There is evidence from the social science literature that human preferences in interpersonal interactions depend partly on a measurable personality trait called, Social Value Orientation (SVO). Automated agents are often written by humans to serve as their delegates when interacting with other agents. Thus, one might expect an agent’s behaviour to be influenced by the SVO of its human designer. With that in mind, we present the following: first, we explore, discuss and provide a solution to the question of how SVO tests that were designed for humans can be used to evaluate agents’ social preferences. Second, we show that in our example domain there is a medium–high positive correlation between the social preferences of agents and their human designers. Third, we exemplify how the SVO information of the designer can be used to improve the performance of some other agents playing against those agents, and lastly, we develop and exemplify the behavioural signature SVO model which allows us to better predict performances when interactions are repeated and behaviour is adapted.     

Picture perfect: Girls’ and boys’ preferences towards visual complexity in children’s websites

This experiment examined children’s aesthetic preferences for websites designed for them. It applied Berlyne’s theory of aesthetic preference to these websites: a theory that suggests that people prefer a medium level of stimuli to a low or high level of stimuli. The experiment used a 2 × 3 between-subject design and involved 45 boys and 45 girls. In the experiment the children were asked to rate 12 children’s learning websites for aesthetic preference. The websites had been classified according to whether they displayed a high, medium or low level of visual complexity. The results of the experiment showed that overall the children preferred websites that displayed a medium level of visual complexity to those that displayed a high or low level of visual complexity. Thus the results supported Berlyne’s theory. However, when the children’s ratings were analysed with respect to their gender, it was found that the boys preferred a high level of visual complexity and the girls preferred a medium or low level of visual complexity. In other words, Berlyne’s theory was partly supported. Further analysis revealed other gender related aesthetic preferences. This paper should be of interest to anyone who designs learning websites for children.     

Stochastic optimality in the portfolio tracking problem involving investor’s temporal preferences

We consider an optimal portfolio selection problem to track a riskless reference portfolio. Portfolio management strategies are compared taking into account the investor’s temporal preferences. We investigate stochastic optimality of the strategy that minimizes the expected long-run cost, deriving an asymptotical upper (almost sure) estimate for the difference between the values of the objective functional corresponding to the optimal strategy and for any admissible control.     

Application of particle swarm optimization algorithm in bellow optimum design

In this paper, Particle Swarm Optimization Algorithm （PSOA） is used in problem of the bellow optimum design with constraints, in which the design variables are discrete. To implement bellow optimum design by PSOA, an augmented objective function is constructed based on penalty function and a new updating scheme of penalty parameter s is proposed. A new Discrete PSOA （DPSOA） is proposed. The mathematic model of bellow optimum design is established. Through numerical examples of bellow design, comparing the results of examples by proposed DPSOA with the theory solutions by Net method, it shows that the particle swarm optimization algorithm can be applied to the bellow optimum design successfully and satisfactory results by DPSOA are obtained, which is discrete optimal solution in the feasible domain.     

Layout design of reinforced concrete structures using two-material topology optimization with Drucker–Prager yield constraints

This paper aims to develop a method that can automatically generate the optimal layout of reinforced concrete structures by incorporating concrete strength constraints into the two-material topology optimization formulation. The Drucker–Prager yield criterion is applied to predict the failure behavior of concrete. By using the power-law interpolation, the proposed optimization model is stated as a minimum compliance problem under the yield stress constraints on concrete elements and the material volume constraint of steel. The ε-relaxation technique is employed to prevent the stress singularity. A hybrid constraint-reduction strategy, in conjunction with the adjoint-variable sensitivity information, is integrated into a gradient-based optimization algorithm to overcome the numerical difficulties that arise from large-scale constraints. It can be concluded from numerical investigations that the proposed model is suitable for obtaining a reasonable layout which makes the best uses of the compressive strength of concrete and the tensile strength of steel. Numerical results also reveal that the hybrid constraint-reduction strategy is effective in solving the topology optimization problems involving a large number of constraints.     

Isogeometric configuration design optimization of shape memory polymer curved beam structures for extremal negative Poisson’s ratio

Using a continuum-based design sensitivity analysis (DSA) method, a configuration design optimization method is developed for curved Kirchhoff beams with shape memory polymers (SMP), from which we systematically synthesize lattice structures achieving target negative Poisson’s ratio. A SMP phenomenological constitutive model for small strains is utilized. A Jaumann strain, based on the geometrically exact beam theory, is additively decomposed into elastic, stored, and thermal parts. Non-homogeneous displacement boundary conditions are employed to impose mechanical loadings. At each equilibrium configuration, an additional nonlinear analysis is performed to calculate the Poisson’s ratio and its design sensitivity of the SMP material. The design objectives are twofold: for purely elastic materials, lattice structures are designed to achieve prescribed Poisson’s ratios under finite compressive deformations. Also, SMP-based lattice structures are synthesized to possess target Poisson’s ratios in specified temperature ranges. The analytical design sensitivity of the Poisson’s ratio is verified through comparison with finite difference sensitivity. Several configuration design optimization examples are demonstrated.     

Neural network’s selection of color in UI design of social software

Neural Computing and Applications - In recent years, the design of social software UI has become a design research focus in the field of design. Color affects many factors in UI design. However,...     

Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

Predicting user’s preferences using neural networks and psychology models

In this paper we describe a new model suitable for optimization problems with explicitly unknown optimization functions using user’s preferences. The model addresses an ability to learn not known optimization functions thus perform also a learning of user’s preferences. The model consists of neural networks using fuzzy membership functions and interactive evolutionary algorithms in the process of learning. Fuzzy membership functions of basic human values and their priorities were prepared by utilizing Schwartz’s model of basic human values (achievement, benevolence, conformity, hedonism, power, security, self-direction, stimulation, tradition and universalism). The quality of the model was tested on “the most attractive font face problem” and it was evaluated using the following criteria: a speed of optimal parameters computation, a precision of achieved results, Wilcoxon signed rank test and a similarity of letter images. The results qualify the developed model as very usable in user’s preference modeling.     

Multi-objective optimization design of a connection frame in macro–micro motion platform

Connection frame is an essential component to implement high acceleration and ultra-precision positioning motion in a macro–micro motion platform. The performance of the positioning system is mainly affected by two sources which include thermal–mechanical deformation and the natural frequency of connection frame. In the paper, multi-objective optimization and design for the connection frame is constructed and discussed comprehensively by the effects of thermal–mechanical deformation and the natural frequency of the system. The optimization objectives for the connection structure are the minimized displacement when thermal–mechanical deformation is occurred, the maximized natural frequency to avoid system resonance, and the light weight for the connection structure to fulfil high acceleration motion. Using response surface method (RSM) combined with finite element method (FEM), the objective function is formulated as a prediction model. Non-dominated Sorting Genetic Algorithm II (NSGAII) is used to solve the optimization model and attain the matched parameters. A cantilever beam example is tested to examine the validity of the methodology, and the results from prediction model agree well with that from theoretical model. By the above methodology, a high performance with optimal parameters for the connection structure is obtained, and its natural frequency and weight can meet our design expectation.     

Structural optimization in magnetic fields using the homogenization design method — Part I

Summary The homogenization design method has been successfully applied to obtain the optimal topology of a structure under electrical fields as well as various mechanical environments. This paper introduces the expansion of the application of the homogenization design method into the structure in magnetic fields. The topology optimization on a device in magnetic fields is focused on the maximizing the magnetic flux in an air-gap. This is accomplished by maximizing the magnetic mean compliance defined by the magnetic energy in a given region of the device. The homogenization design method is applied to three-dimensional cases with and without considering the saturation effect of the material. Results show that the homogenization design method is valid to maximize the vector potential in linear cases and the magnetic flux in non-linear cases.