Human lifting simulation using a multi-objective optimization approach

This paper presents a multiobjective optimization (MOO) approach to predicting dynamic lifting for a three-dimensional, highly redundant digital human model with 55 degrees of freedom. The optimization problem is formulated to optimize two objective functions simultaneously—dynamic effort and stability—subject to basic physical and kinematical constraints. The predictive dynamics approach is used to solve for the joint angles, torque profiles, and ground reaction forces. The weighted sum approach of MOO is used to aggregate the two objective functions, and the Pareto optimal set for the problem is generated by systematically varying the weighting parameters for the objective functions. Experimental data are used to validate the final simulation. Several examples are presented to demonstrate the effect of the weighting parameters for the two objective functions on the predicted box-lifting strategies. The results show that the proposed MOO approach improves the simulation results compared to the single objective optimization formulation. Also, the formulation is less sensitive to the weighting coefficient for the stability criterion.     

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.     

Physical programming for preference driven evolutionary multi-objective optimization

Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization.     

A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

This paper proposes a novel multi-objective root system growth optimizer (MORSGO) for the copper strip burdening optimization. The MORSGO aims to handle multi-objective problems with satisfactory convergence and diversity via implementing adaptive root growth operators with a pool of multi-objective search rules and strategies. Specifically, the single-objective root growth operators including branching, regrowing and auxin-based tropisms are deliberately designed. They have merits of appropriately balancing exploring & exploiting and self-adaptively varying population size to reduce redundant computation. The effective multi-objective strategies including the fast non-dominated sorting and the farthest-candidate selection are developed for saving and retrieving the Pareto optimal solutions with remarkable approximation as well as uniform spread of Pareto-optimal solutions. With comprehensive evaluation against a suit of benchmark functions, the MORSGO is verified experimentally to be superior or at least comparable to its competitors in terms of the IGD and HV metrics. The MORSGO is then validated to solve the real-world copper strip burdening optimization with different elements. Computation results verifies the potential and effectiveness of the MORSGO to resolve complex industrial process optimization.     

A novel group search optimizer for multi-objective optimization

In this paper, a novel multi-objective group search optimizer named NMGSO is proposed for solving the multi-objective optimization problems. To simplify the computation, the scanning strategy of the original GSO is replaced by the limited pattern search procedure. To enrich the search behavior of the rangers, a special mutation with a controlling probability is designed to balance the exploration and exploitation at different searching stages and randomness is introduced in determining the coefficients of members to enhance the diversity. To handle multiple objectives, the non-dominated sorting scheme and multiple producers are used in the algorithm. In addition, the kernel density estimator is used to keep diversity. Simulation results based on a set of benchmark functions and comparisons with some methods demonstrate the effectiveness and robustness of the proposed algorithm, especially for the high-dimensional problems.     

Nonlinear channel equalizer design using directional evolutionary multi-objective optimization

In this paper, a new equalizer learning scheme is introduced based on the algorithm of the directional evolutionary multi-objective optimization (EMOO). Whilst nonlinear channel equalizers such as the radial basis function (RBF) equalizers have been widely studied to combat the linear and nonlinear distortions in the modern communication systems, most of them do not take into account the equalizers’ generalization capabilities. In this paper, equalizers are designed aiming at improving their generalization capabilities. It is proposed that this objective can be achieved by treating the equalizer design problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets, followed by deriving equalizers with good capabilities of recovering the signals for all the training sets. Conventional EMOO which is widely applied in the MOO problems suffers from disadvantages such as slow convergence speed. Directional EMOO improves the computational efficiency of the conventional EMOO by explicitly making use of the directional information. The new equalizer learning scheme based on the directional EMOO is applied to the RBF equalizer design. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good generalization capabilities, i.e., good performance on predicting the unseen samples.     

A novel approach to combine features for salient object detection using constrained particle swarm optimization

Despite significant amount of research works, the best available visual attention models still lag far behind human performance in predicting salient object. In this paper, we present a novel approach to detect a salient object which involves two phases. In the first phase, three features such as multi-scale contrast, center-surround histogram and color spatial distribution are obtained as described in Liu et al. model. Constrained Particle Swarm Optimization is used in the second phase to determine an optimal weight vector to combine these features to obtain saliency map to distinguish a salient object from the image background. To achieve this, we defined a simple fitness function which highlights a salient object region with well-defined boundary and effectively suppresses the background regions in an image. The performance is evaluated both qualitatively and quantitatively on a publicly available dataset. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of precision, recall, F -measure and area under curve.     

A multi-objective nonlinear optimization approach to designing effective air quality control policies

This paper presents the implementation of a two-objective optimization methodology to select effective tropospheric ozone pollution control strategies on a mesoscale domain. The objectives considered are (a) the emission reduction cost and (b) the Air Quality Index. The control variables are the precursor emission reductions due to available technologies. The nonlinear relationship linking air quality objective and precursor emissions is described by artificial neural networks, identified by processing deterministic Chemical Transport Modeling system simulations. Pareto optimal solutions are calculated with the Weighted Sum Strategy. The two-objective problem has been applied to a complex domain in Northern Italy, including the Milan metropolitan area, a region characterized by frequent and persistent ozone episodes.     

On multi-objective optimization of geometry of staggered herringbone micromixer

A design methodology for micromixers is presented which systematically integrates computational fluid dynamics (CFD) with an optimization methodology based on the use of design of experiments (DOE), function approximation technique (FA) and multi-objective genetic algorithm (MOGA). The methodology allows the simultaneous investigation of the effect of geometric parameters on the mixing performance of micromixers whose design strategy is based fundamentally on the generation of chaotic advection. The methodology has been applied on a Staggered Herringbone Micromixer (SHM) at several Reynolds numbers. The geometric features of the SHM are optimized and their effects on mixing are evaluated. The degree of mixing and the pressure drop are the performance criteria to define the efficiency of the micromixer for different design requirements.     

Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm

This research is based on a new hybrid approach, which deals with the improvement of shape optimization process. The objective is to contribute to the development of more efficient shape optimization approaches in an integrated optimal topology and shape optimization area with the help of genetic algorithms and robustness issues. An improved genetic algorithm is introduced to solve multi-objective shape design optimization problems. The specific issue of this research is to overcome the limitations caused by larger population of solutions in the pure multi-objective genetic algorithm. The combination of genetic algorithm with robust parameter design through a smaller population of individuals results in a solution that leads to better parameter values for design optimization problems. The effectiveness of the proposed hybrid approach is illustrated and evaluated with test problems taken from literature. It is also shown that the proposed approach can be used as first stage in other multi-objective genetic algorithms to enhance the performance of genetic algorithms. Finally, the shape optimization of a vehicle component is presented to illustrate how the present approach can be applied for solving multi-objective shape design optimization problems.     

Lifting motion simulation using a hybrid approach

In this study, a hybrid dynamic model for lifting motion simulation is presented. The human body is represented by a two-dimensional (2D) five-segment model. The lifting motions are predicted by solving a nonlinear optimisation problem, the objective function of which is defined based on a minimal-effort performance criterion. In the optimisation procedure, the joint angular velocities are bounded by time-functional constraints that are determined by actual motions. Symmetric lifting motions performed by younger and older adults under varied task conditions were simulated. Comparisons between the simulation results and actual motion data were made for model evaluation. The results showed that the mean and median joint angle errors were less than 10°, which suggests the proposed model is able to accurately simulate 2D lifting motions. The proposed model is also comparable with the existing motion simulation models in terms of the prediction accuracy. Strengths and limitations of this hybrid model are discussed.     

An efficient multi-objective optimization method for black-box functions using sequential approximate technique

Multi-objective optimization problems in practical engineering usually involve expensive black-box functions. How to reduce the number of function evaluations at a good approximation of Pareto frontier has been a crucial issue. To this aim, an efficient multi-objective optimization method based on a sequential approximate technique is suggested in this paper. In each iteration, according to the prediction of radial basis function with a micro multi-objective genetic algorithm, an extended trust region updating strategy is adopted to adjust the design region, a sample inheriting strategy is presented to reduce the number of new function evaluations, and then a local-densifying strategy is proposed to improve the accuracy of approximations in concerned regions. At the end of each iteration, the obtained actual Pareto optimal points are stored in an external archive and are updated as the iteration process. The effect of the present method is demonstrated by eight test functions. Finally, it is employed to perform the structure optimization of a vehicle door.     

Simulation based optimization of a train maintenance facility

In this paper, a simulation based optimization method is developed for optimization of scheduling policies. This method uses the technique of coupling industrial simulation software with a multi-objective optimizer based on genetic algorithms. It is used to optimize the performances of a railway maintenance facility by choosing the best scheduling policy. Numerical results show that a significant improvement is achieved with respect to the simulation results of the existing system. The method adapted by our problem can be extended to deal with the selection of scheduling rules in using other types of simulation models.     

Illustration of fairness in evolutionary multi-objective optimization

It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness. This mechanism tries to balance the number of offspring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present illustrative examples to point out situations, where the right mechanism can speed up the optimization process significantly. We also indicate drawbacks for the use of fairness by presenting instances, where the optimization process is slowed down drastically.     

A mathematical framework for passenger screening optimization via a multi-objective evolutionary approach

Concerns regarding the smuggling of dangerous items into commercial flights escalated after the failed Christmas day bomber attack. As a result, the Transportation Security Agency (TSA) has strengthened its efforts to detect passengers carrying hazardous items by installing novel screening technologies and by increasing the number of random pat-downs performed at security checkpoints nationwide. However, the implementation of such measures has raised privacy and health concerns among different groups thus making the design and evaluation of new inspection strategies strongly necessary. This research presents a mathematical framework to design passenger inspection strategies that include the utilization of novel and traditional technologies (i.e. body scanners, explosive detection systems, explosive trace detectors, walk-through metal detectors, and wands) offered by multiple manufacturers, to identify three types of items: metallic, bulk explosives (i.e. plastic, liquids, gels), and traces of explosives. A multiple objective optimization model is proposed to optimize inspection security, inspection cost, and processing time; an evolutionary approach is used to solve the model. The result is a Pareto set of quasi-optimal solutions representing multiple inspection strategies. Each strategy is different in terms of: (1) configuration, (2) the screening technologies included, (3) threshold calibration, and consequently, (4) inspection security, inspection cost, and processing time.     

Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization

This paper presents a fuzzy-Pareto dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization (MOPSO). This multi-objective planning model captures the possibilistic variations of the system loads using a fuzzy triangular number. The MOPSO based on the Pareto-optimality principle is used to obtain a set of non-dominated solutions representing different network structures under uncertainties in load demands and these non-dominated solutions are stored in an elite archive of limited size. Normally, choosing the candidate non-dominated solutions to be retained in the elite archive while maintaining the quality of the Pareto-approximation front as well as maintaining the diversity of solutions on this front is very much computationally demanding. In this paper, the principles of fuzzy Pareto-dominance are used to find out and rank the non-dominated solutions on the Pareto-approximation front. This ranking in turn is used to maintain the elite archive of limited size by discarding the lower ranked solutions. The two planning objectives are: (i) minimization of total installation and operational cost and (ii) minimization of risk factor. The risk factor is defined as a function of an index called contingency-load-loss index (CLLI), which captures the effect of load loss under contingencies, and the degree of network constraint violations. The minimization of the CLLI improves network reliability. The network variables that are optimized are: (i) number of feeders and their routes, and (ii) number and locations of sectionalizing switches. An MOPSO (developed by the authors), based on a novel technique for the selection and assignment of leaders/guides for efficient search of non-dominated solutions, is used as the optimization tool. The proposed planning approach is validated on a typical 100-node distribution system. Performance comparisons between the planning approaches with the possibilistic and deterministic load models are provided highlighting the relative merits and demerits. It is also verified that the proposed solution ranking scheme based on the fuzzy-Pareto dominance is very much better from both quality and computational burden point of view in comparison with the other well-known archive truncation techniques based on clustering and solution density measurement etc.     

Worker training: the fallacy of a single,correct lifting technique

The lifting stress calculator (LSC) is a user-friendly device based upon a two-dimensional model of static lifting; it recommends the optimum method of lifting given the anthropometry and clinical symptoms, and the physical characteristics of the load. This study illustrates the fact that there is no one simple, safe and proper lifting technique and that each lifting task warrants an individualized evaluation. The use of the LSC in the areas of rehabilitation, training and workplace design is advocated.     

Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)

In this paper, fuzzy threshold values, instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a single inverted pendulum having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the normalized summation of rising time and overshoot of cart (S_RO–C) and the normalized summation of rising time and overshoot of pendulum (S_RO–P) in the deterministic approach. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for single inverted pendulum problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain. Such Pareto front is then obtained for single inverted pendulum having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Monte Carlo simulation (MCS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA with fuzzy threshold values includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.     

Forward kinematics,performance analysis,and multi-objective optimization of a bio-inspired parallel manipulator

In this paper, a bio-inspired parallel manipulator with one translation along z-axis and two rotations along x- and y- axes is developed as the hybrid head mechanism of a groundhog robotic system. Several important issues including forward kinematic modeling, performance mapping, and multi-objective improvement are investigated with specific methods or technologies. Accordingly, the forward kinematics is addressed based on the integration of radial basis function network and inverse kinematics. A novel performance index called dexterous stiffness is defined, derived and mapped. The multi-objective optimization with particle swarm algorithm is conducted to search for the optimal dexterous stiffness and reachable workspace.     

Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index

Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.