An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization

This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.     

Hybrid genetic optimization for design of photonic crystal emitters

A unique hybrid-optimization technique is proposed, based on genetic algorithms (GA) and gradient descent (GD) methods, for the smart design of photonic crystal (PhC) emitters. The photonic simulation is described and the granularity of photonic crystal dimensions is considered. An innovative sliding-window method for performing local heuristic search is demonstrated. Finally, the application of the proposed method on two case studies for the design of a multi-pixel photonic crystal emitter and the design of thermal emitter in thermal photovoltaic is demonstrated. Discussion in the report includes the ability of the optimal PhC structures designed using the proposed method, to produce unprecedented high emission efficiencies of 54.5% in a significantly long wavelength region and 84.9% at significantly short wavelength region.     

可靠性优化的一种新的算法

建立了可靠性冗余优化模型,提出了一种基于粒子群优化算法的可靠性优化的新方法,该方法结合了遗传算法的思想。实例结果表明,粒子群算法比模拟退火算法和遗传算法效果好。     

A two-step heuristic algorithm for layout design of unequal-sized facilities with input/output points

The facility layout problem (FLP) is to arrange facilities within a given area so as to minimise the total material-handling cost. An efficient arrangement of facilities plays an important part in the achievement of high productivity in a manufacturing system. This paper focuses on the layout design of unequal-sized facilities with fixed shapes and input/output (I/O) points. A mixed-integer programming (MIP) model is developed to obtain optimal solutions. Since the MIP model is not applicable to large-sized problems, a two-step heuristic algorithm is developed to solve the FLPs. In the first step, a layout solution with moderate quality is generated by using an interconnected zone algorithm and a simulated annealing (SA) algorithm. The zone algorithm is a newly developed layout construction technique which places facilities successively within designated zones according to a placing sequence. The zone concept is introduced to reduce the solution space such that the computational effort is decreased, while SA is used to search for the placing sequences. The second step improves that solution further using the reduced MIP by employing the relative position information obtained from the first step. Computational experiments indicate that the algorithm in the first step can yield on average a 1.048% improvement on the best solutions obtained by the existing algorithms. The second step can further improve the solutions obtained in the first step by 0.484% on average. Compared to the commercial software VIP-PLANOPT 2006 (Engineering Optimization Software 2005 Engineering Optimization Software. 2005. PLANOPT User’s Manual, Florida: Deltona.  [Google Scholar]), the proposed two-step algorithm improves the solutions by 9.113% on average.     

Minimising total weighted earliness and tardiness on parallel machines using a hybrid heuristic

Scheduling jobs with different processing times and distinct known due dates on parallel machines (which may be idle) so as to minimise the total weighted earliness and tardiness is NP-hard. It occurs frequently in industrial settings with a just-in-time production environment. Here, small instances of this problem are tackled via an exact mixed-integer program, whereas larger instances are approximately solved using hybrid algorithms. The computational investigation discerns what makes a difficult instance, and demonstrates the efficiency of the approach.     

Applying a hybrid simulated annealing and tabu search approach to non-permutation flowshop scheduling problems

Researchers have indicated that a permutation schedule can be improved by a non-permutation schedule in a flowshop with completion time-based criteria, such as makespan and total completion time. This study proposes a hybrid approach which draws on the advantages of simulated annealing and tabu search for the non-permutation flowshop scheduling problem, in which the objective function is the makespan of the schedule. To verify the effectiveness of the proposed hybrid approach, computational experiments are performed on a set of well-known non-permutation flowshop scheduling benchmark problems. The result shows that the performance of the hybrid approach is better than that of other approaches, including ant colony optimisation, simulated annealing, and tabu search. Further, the proposed approach found new upper bound values for all benchmark problems within a reasonable computational time.     

Applying tabu search and simulated annealing to the optimal design of sewer networks

Optimizations of sewer network designs create complicated and highly nonlinear problems wherein conventional optimization techniques often get easily bogged down in local optima and cannot successfully address such problems. In the past decades, heuristic algorithms possessing robust and efficient global search capabilities have helped to solve continuous and discrete optimization problems and have demonstrated considerable promise. This study applied tabu search (TS) and simulated annealing (SA) to the optimization of sewer network designs. For a case study, this article used the sewer network design of a central Taiwan township, which contains significantly varied elevations, and the optimal designs from TS and SA were compared with the original official design. The results show that, in contrast with the original design's failure to satisfy the minimum flow-velocity requirements, both TS and SA achieved least-cost solutions that also fulfilled all the constraints of the design criteria. According to the average performance of 200 trials, SA outperformed TS in both robustness and efficiency for solving sewer network optimization problems.     

Optimization of cardinality constrained portfolios with a hybrid local search algorithm

One of the main advantages of portfolios over single assets is that risk can be diversified without necessarily reducing the expected return - provided "proper" assets are selected and they are assigned the "proper" weights. Since in practice investors tend to restrict themselves to a rather small number of different assets, the decision which securities to include is a crucial one that turns out to be NP-hard. In this paper we suggest a hybrid local search algorithm which combines principles of Simulated Annealing and evolutionary strategies and which proved to highly efficiently approach this problem. Correspondence to: Dietmar MaringerThe authors would like to thank participants of the 4^th Metaheuristics International Conference and the Econometrics Research Seminar at the University of Geneva, U. Derigs, M. Gilli, H.-O. Günther, and two anonymous referees for valuable comments on earlier versions of this paper.     

Two-edge disjoint survivable network design problem with relays: a hybrid genetic algorithm and Lagrangian heuristic approach

This article presents a network design problem with relays considering the two-edge network connectivity. The problem arises in telecommunications and logistic networks where a constraint is imposed on the distance that a commodity can travel on a route without being processed by a relay, and the survivability of the network is critical in case of a component failure. The network design problem involves selecting two-edge disjoint paths between source and destination node pairs and determining the location of relays to minimize the network design cost. The formulated problem is solved by a hybrid approach of a genetic algorithm (GA) and a Lagrangian heuristic such that the GA searches for two-edge disjoint paths for each commodity, and the Lagrangian heuristic is used to determine relays on these paths. The performance of the proposed hybrid approach is compared to the previous approaches from the literature, with promising results.     

A hybrid optimization method of harmony search and opposition-based learning

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optimum in an efficient way. In this article, a hybrid optimization approach is proposed and studied, in which the HS is merged together with the opposition-based learning (OBL). The modified HS, namely HS-OBL, has an improved convergence property. Optimization of 24 typical benchmark functions and an optimal wind generator design case study demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.     

Lightweight and crashworthiness design of an electric vehicle using a six-sigma robust design optimization method

Design optimization plays an important role in electric vehicle (EV) design. However, fluctuations in design variables and noise factors during the forming process affect the stability of optimization results. This study uses six-sigma robust design optimization to explore the lightweight design and crashworthiness of EVs with uncertainty. A full-scale finite element model of an EV is established. Then, multi-objective design optimization is performed by integrating optimal Latin hypercube sampling, radial basis functions and non-dominated sorting genetic algorithm-II to achieve minimum peak acceleration and mass. Finally, six-sigma robust optimization designs are applied to improve the reliability and sigma level. Robust optimization using adaptive importance sampling is shown to be more efficient than that using Monte Carlo sampling. Moreover, deformation of the battery compartment and peak acceleration of the B-pillar are greatly decreased. The EV’s safety performance is improved and the lightweight effect is remarkable, validating the strong engineering practicability of the method.     

A variant space search method and its application to the optimization design of compressor profiles

Optimization methods have been widely used in practical engineering, with search efficiency and global search ability being the main evaluation criteria. In this article, the Bezier curve equivalent recursion is used in a genetic algorithm (GA) to realize the variant space search to improve the search efficiency and global search ability. The parameters related to this method are investigated by an optimization test of the simple curve approximation, which is then used for optimization designs of supersonic and transonic profiles. The results show that the GA can be improved if the variant space search method is added.     

Theoretical basis of a simulation package for the integrated optimisation of profile and trajectory of satellite launch vehicles

Introducing the concepts of set-theoretic sequencing logic, hierarchic optimisation and sensitivity-directed iterative simulation, this paper provides a theoretical basis for the development of a simulation package for the three-dimensional atmospheric controlled flight of satellite launch vehicles subject to specified control constraints. The approach described here enables the optimisation of the profile of the vehicle to be carried out together with its trajectory, given the orbital objectives. The simulation package, SIMSPACE II, developed on this basis, is shown to have features superior to the MATS program and related packages.     

Inverse identification of elastic properties of composite materials using hybrid GA-ACO-PSO algorithm

The main emphasis of this paper is placed on the effectiveness of the proposed optimization method in material identification. The primary motivation of integrating GA, ACO and PSO is to minimize each other’s weaknesses and to promote respective strengths. In the proposed algorithm, the effect of random initialization of GA is subdued by passing the products of GA through the ACO and PSO operators to well organize the exploitative and exploratory search coverage. In return, GA improves the convergence rate and alleviates the strong dependency on the pheromone array in ACO as well as resolves the conflict arisen in identifying the trade-off parameter and further refine the exploitative search of PSO with the introduction of two-point standard mutation and one-point refined mutation. The proposed algorithm has been verified and applied in composite material identification with absolute percentage errors between measured and evaluated natural frequencies not more than 2%.     

Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm

Constrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.     

基于改进模拟退火算法的复合材料层合板频率优化

针对复合材料层合板频率优化问题,结合可行规则法和直接搜索模拟退化算法,提出了一种自适应模拟退火(SA)改进算法。层合板优化目标是基频、频率带隙以及给定基频和带隙约束的层合板厚度。设计变量包括铺层角度和铺层数两种离散变量。改进算法的自适应新点产生模块采用依赖温度的动态调整搜索半径,改善了直接搜索模拟退化(DSA)算法易陷入局部极值的缺陷,而可行规则法的引入提高了SA算法求解约束问题的效率和简易性。采用Ritz法进行频率响应分析以考虑弯扭耦合影响。不同铺层数、角度增量和长宽比时的层合板3类算例结果显示:改进算法能有效求解层合板频率优化,可获得更多或更好的铺层顺序全局优化解。     

Analysis,design and optimization of structures using force method and genetic algorithm

In the first part of this paper, the energy formulation of the force method is presented and analysis is performed using genetic algorithm. Two simple examples are provided to show the accuracy of the approach. In the second part, an efficient method is developed for designing structures with prescribed stress ratios for its members. The genetic algorithm performed very well and designs with specified stress ratios were achieved with a good convergence rate. A unit value of c_i for all the members of a structure corresponds to the well known fully stressed design. In the third part, minimum weight design is formulated by the additional conditions being imposed on the design process. Again, genetic algorithm showed to be a powerful means for optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

Multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm

This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bézier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier–Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.     

压电智能连续体结构/控制一体化广义拓扑优化

对于以往较少涉及到的同时考虑结构拓扑、作动器位置与数目和控制器参数等多种优化设计变量参与的压电智能板结构的一体化优化设计问题,研究了结构/控制一体化广义拓扑优化设计的方法。提出采用基于耦合模态空间的二次型最优控制系统设计与基于遗传算法和数学形态学处理的策略进行一体化拓扑优化设计实现。数值算例的结果表明,所提方法合理、有效,能够得到清晰的结构拓扑和良好的可控性。     

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

A territorial-based filtering algorithm (TBFA) is proposed as an integration tool in a multi-level design optimization methodology. The design evaluation burden is split between low- and high-cost levels in order to properly balance the cost and required accuracy in different design stages, based on the characteristics and requirements of the case at hand. TBFA is in charge of connecting those levels by selecting a given number of geometrically different promising solutions from the low-cost level to be evaluated in the high-cost level. Two test case studies, a Francis runner and a transonic fan rotor, have demonstrated the robustness and functionality of TBFA in real industrial optimization problems.