An evolutionary algorithm for the optimal design of inductionmotors

This paper describes the application of an evolutionary algorithm to the design of induction motors. It is shown that the use of an evolutionary algorithm offers advantages over other approaches. These include a high rate of global convergence and the ability to handle discrete variables     

Quantifying the impact of inland transport times on container fleet sizing in liner shipping services with uncertainties

Container fleet sizing is a key issue in liner shipping industry. Although container shipping is an intermodal transport system, inland container movements are often beyond the control of shipping lines. It is vital to understand how the inland transport times and their variability affect the container fleet sizing. This paper first formulates the container fleet sizing problem in liner services with uncertain customer demands and stochastic inland transport times. Simulation-based optimisation approaches are then employed to solve the problem. Two typical shipping services, one cyclic route in trans-Pacific lane and the other more complicated route in Europe–Asia lane, are used as case studies. A quantitative relationship between the optimal container fleet size and the inland transport time is established. The impact of uncertainties in inland times on the fleet sizing is also investigated. The results provide shipping companies useful insights into making strategic decisions.     

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

Hybrid algorithm of differential evolution and evolutionary programming for optimal reactive power flow

Differential evolution (DE) is a promising evolutionary algorithm for solving the optimal reactive power flow (ORPF) problem, but it requires relatively large population size to avoid premature convergence, which will increase the computational time. On the other hand, evolutionary programming (EP) has been proved to have good global search ability. Exploiting this complementary feature, a hybrid algorithm of DE and EP, denoted as DEEP, is proposed in this study to reduce the required population size. The hybridisation is designed as a novel primary-auxiliary model to minimise the additional computational cost. The effectiveness of DEEP is verified by the serial simulations on the IEEE 14-, 30-, 57-bus system test cases and the parallel simulations on the IEEE 118-bus system test case.     

Using an evolutionary algorithm for catalog design

This paper describes an evolutionary algorithm that was developed for catalog design. This algorithm is based on genetic algorithms, but uses an object-oriented coding scheme to represent a design, and introduces unique crossover and mutation operators. To account for the dependence of system performance on both system configuration and component selection, the evolutionary algorithm allows for simultaneous alterations of configurations and components. This new approach allows the consideration of alternate configurations and allows the configurations to evolve to make the best use of the available components. Using this evolutionary algorithm, a piping system was designed in which cooling fluid was delivered to three machines on a manufacturing floor at specified pressures and flow rates. The algorithm was able to find good designs that satisfied the given design specifications.     

The effect of safety climate on seafarers’ safety behaviors in container shipping

This study empirically examined safety climate and its effects on safety behaviors from seafarers’ perceptions in the container shipping context. Research hypotheses were formulated and tested using survey data collected from 608 seafarers working on 124 vessels belonging to 13 of the top 20 global container carriers. A structural equation model was used to examine the effect of safety climate dimensions, namely, safety policy, perceived supervisor safety behavior, and safety management, on safety behavior. The results revealed a positive association between safety climate and seafarers’ safety behavior. The contribution of the study findings to the development of safety climate theory and their managerial implications for vessel safety in shipping operations are discussed.     

The effects of safety climate on vessel accidents in the container shipping context

This study empirically evaluates the influence of safety climate on vessel accidents from a seafarer's perspective, specifically in the container shipping context. Factor analysis revealed six safety climate dimensions: management safety practices, supervisor safety practices, safety attitude, safety training, job safety, and co-workers' safety practices. Logistic regression analysis was used to evaluate the effects of safety climate dimensions on vessel accidents in respect of crew fatality and vessel failure. Study findings indicated that management safety practices, safety training, and job safety dimensions significantly affect crew fatality incidence, and the job safety dimension has a significant influence on vessel failure. Overall, results suggest the job safety dimension has the most important effect on vessel accidents, followed by management safety practices and safety training dimensions. Theoretical and practical implications of the findings for vessel accident prevention in the container shipping context are discussed.     

Dynamic determination of vessel speed and selection of bunkering ports for liner shipping under stochastic environment

In this work, we study a liner shipping operational problem which considers how to dynamically determine the vessel speed and refueling decisions, for a single vessel in one service route. Our model is a multi-stage dynamic model, where the stochastic nature of the bunker prices is represented by a scenario tree structure. Also, we explicitly incorporate the uncertainty of bunker consumption rates into our model. As the model is a large-scale mixed integer programming model, we adopt a modified rolling horizon method to tackle the problem. Numerical results show that our framework provides a lower overall cost and more reliable schedule compared with the stationary model of a related work.     

Mathematical models and solution methods for optimal container terminal yard layouts

In this paper, we introduce an integer linear program for planning the layout of container yards. We concentrate on a special layout class of container yards which we call yard layout with transfer lanes. For those layouts typically rubber tired gantry cranes are used for stacking operations and trucks for horizontal transports. We show that the optimization model can be formulated as a special type of a resource constrained shortest path problem for which the LP relaxation always has at least one integer optimal solution. This model is restricted to a rectangular storage yard which allows a linear formulation. For an arbitrary shaped container yard we adopt the model and develop a variable neighborhood descent (VND) heuristic for solving non-rectangular instances. Concerning the rectangular case, we show that the VND heuristic achieves optimal solutions for 38% of the realistic test instances.     

Interval algorithm for global numerical optimization

The interval optimization algorithm shows great advantages in bound constrained global optimization. An interval algorithm is presented in this article based on a new selection criterion. The selection criterion is proposed based on numerical experiments and the parameter pf* designed by Casado, Garcia and Csendes in 2000. The proposed criterion at each iteration selects some intervals of which the number is not greater than a constant so that the possible memory problem during the implementation of the algorithm is avoided and the running time of the algorithm is decreased, when the dimension of the problem is increasing. Based on the selection criterion, the proposed algorithm is implemented for a wide set of tested functions which includes easy and hard problems. Numerical experiments show that the proposed algorithm is efficient.     

A hybrid evolutionary algorithm for distribution feeder reconfiguration

Distribution feeder reconfiguration (DFR) is formulated as a multi-objective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance (λ ₂ norm) between the vector-valued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.     

Real-parameter quantum evolutionary algorithm for economic load dispatch

A novel real-parameter optimisation algorithm called the 'real-parameter quantum evolutionary algorithm' is presented. The algorithm pieces together the ideas from evolutionary algorithms (EA) and quantum computing to provide a robust optimisation technique that can be utilised to optimise highly constrained non-linear real-parameter functions. Quantum bits have immense representational power due to their being in superposition of all the basic states at the same time. New quantum operators designed in this work enable the search to effectively handle the twin objectives of exploitation and exploration. This enables the search to be pursued with small population sizes, thereby speeding up the search process and also ensuring that there is no problem of premature convergence that often plagues pure EA implementations. The power of the proposed algorithm is demonstrated by solving the economic load dispatch (ELD) in power systems. ELD is to find the optimal loadings on the generators so as to achieve minimum operating cost while satisfying various system and unit-level constraints. The proposed method has been applied to standard load dispatch problems reported in the literature including the IEEE 30 bus system, IEEE 57 bus system and a 110-generator problem, and its performance has been compared with the results obtained by other methods. The results adequately demonstrate the enhanced search power of the proposed algorithm in terms of obtaining better solutions and provide motivation for its application to other real-parameter optimisation problems in power systems.     

Local and global strategies for optimal selective mass scaling

The problem of optimal selective mass scaling for linearized elasto-dynamics is discussed. Optimal selective mass scaling should provide solutions for dynamical problems that are close to the ones obtained with a lumped mass matrix, but at much smaller computational costs. It should be equally applicable to all structurally relevant load cases. The three main optimality criteria, namely eigenmode preservation, small number of non-zero entries and good conditioning of the mass matrix are explicitly formulated in the article. An example of optimal mass scaling which relies on redistribution of mass on a global system level is constructed. Alternative local mass scaling strategies are proposed and compared with existing methods using one modal and two transient numerical examples.     

Scheduling trucks in container terminals using a genetic algorithm

Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other six GAs.     

集装箱装箱问题的同质块禁忌搜索算法

为保证集装箱装入物体的稳定性和减少装满后箱内的零碎剩余空间,提出了由同种规格物体组合成同质块的思想,利用局部启发规则构造同质块.设计了有效的剩余空间划分与合并处理方法,以便最大程度利用剩余空间.基于同质块生成和剩余空间处理方法,给出了禁忌搜索算法及其在编码、解码、邻域解构造以及解的评估函数等技术中的实现方法.通过标准实...     

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.     

Asymptotically optimal algorithm for detecting weak gravitational bursts

A new asymptotically optimal algorithm is proposed for detecting an incoherent sequence of weak and infrequent bursts of gravitational radiation under conditions when the quadrature components of the narrow-band process at the output of cryogenic resonant gravitational antennas undergo optimal processing. Translated from Izmeritel'naya Tekhnika, No. 8, pp. 16–18, August, 2000.     

Inverse algorithm for optimal processing of composite materials

During thermoset composite materials processing, the chemical reaction is highly exothermic and because of the low thermal conductivity of the material, significant temperature and state of cure gradients can be generated in thick parts. This creates non-uniform stresses that provoke defects. We propose to control the transformation by monitoring the temperature of the mold walls. A general inverse analysis based on the conjugate gradient method of minimization associated to the adjoint equations is used. After having detailed the method, we propose two examples. The first one presents an optimal cycle to obtain uniform conversion at the end of the curing of an epoxy/glass-fiber composite. The second example is concerned with the control of the temperature variations during the curing of a polyester/glass-fiber composite. The method is experimentally validated and proves to be very powerful and flexible.