Solving algorithm for TA optimization model based on ACO-SA

An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.

FMS Scheduling Simulation Based on an Evolution Algorithm

A FMS (flexible manufacturing system)scheduling algorithm based on an evolution algorithm (EA) is developed by intensively analyzing and researching the scheduling method in this paper.Many factors related to FMS scheduling are considered sufficiently.New explanations for a common kind of the encoding model are given.The rationality of encoding model is ensured by designing a set of new encoding methods,while the simulation experiment is performed.The results show that a FMS scheduling optimum problem with multi-constraint conditions can be effectively solved by a FMS scheduling simulation model based on EA.Compating this method with others,this algorithm has the advantage of good stability and quick convergence.

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

NEW MULTIOBJECTIVE SIMULATED ANNEALING ALGORITHM AND ITS APPLICATION TO TURBINE CASCADES’ DESIGN

A new kind of multiobjective simulated annealing algorithm is proposed,in which the concept of non dominated character is introduced and a new multiobjective acceptance criterion is set up.The optimization example of a typical mathematical problem with two minimum objective functions indicates that all of the solutions contract to the set of the non dominated points,and the variation trend of the optimal solutions is verified to be identical with that obtained using Genetic Algor thms.The new developed algorithm is then applied to the multiobjective optimization design of turbine cascades,in which it is coupled with the aerodynamics computation of the cascade flow fields and performance and the calculated loss coefficient and work potential of the cascade are considered as the objective functions,thus setting up a technique to the engineering optimization design for the cascades.The optimization results,by the view of a group of optimal solutions,show that the algorithm is superior to the traditional technique of multiobjective optimization design and can be applied to more than two objective optimization cascade design problem or other engineering multiobjective optimization designs.

A computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory

Although emission spectral tomography （EST） combines emission spectral measurement with optical computed tomography （OCT）, it is difficult to gain transient emission data from a large number of views, therefore, high precision OCT algorithms with few views ought to be studied for EST application. To improve the reconstruction precision in the case of few views, a new computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory （multi-criterion simulated annealing reconstruction technique, MCSART） is proposed. This algorithm can suffice criterion of least squares, criterion of most uniformity, and criterion of most smoothness synchronously. We can get global optimal solution by MCSART algorithm with simulated annealing theory. The simulating experiment result shows that this algorithm is superior to the traditional algorithms under various noises.

Optimization of remanufacturing process routes oriented toward eco-efficiency

Remanufacturing route optimization is crucial in remanufacturing production because it exerts a considerable impact on the eco-efficiency(i.e.,the best link between economic and environmental benefits)of remanufacturing.Therefore,an optimization model for remanufacturing process routes oriented toward eco-efficiency is proposed.In this model,fault tree analysis is used to extract the characteristic factors of used products.The ICAM definition method is utilized to design alternative remanufacturing process routes for the used products.Afterward,an eco-efficiency objective function model is established,and simulated annealing(SA)particle swarm optimization(PSO)is applied to select the manufacturing process route with the best eco-efficiency.The proposed model is then applied to the remanufacturing of a used helical cylindrical gear,and optimization of the remanufacturing process route is realized by MATLAB programming.The proposed model’s feasibility is verified by comparing the model’s performance with that of standard SA and PSO.

Mechanical Characteristics Analysis of 3D-printing Novel Chiral Honeycomb Array Structures Based on Functional Principle and Constitutive Relationship

Four novel chiral honeycomb structures inspired by the biological arrangement shape are designed.The functional principle is raised to solve the large deformation of bio-inspired structures and the structural constitutive model is proposed to explain the quasi-static mechanical properties of chiral honeycomb array structures and honeycomb structures.Simulation and experiment results verify the accuracy of theoretical analysis results and the errors are all within 15%.In structural mechanical properties,Equidimensional Chiral Honeycomb Array Structure(ECHS)has excellent mechanical properties.Among ECHS,Small-sized Column Chiral Honeycomb Array Structure(SCHCS)has the best properties.The bearing capacity,specific energy absorption,and specific strength of SCHCS are more than twice as much as the others in this paper.The chiral honeycomb array structure has the best mechanical properties at a certain size.In the structural design,the optimal size model should be obtained first in combination with the optimization algorithm for the protection design.