Terahertz spectroscopic uncertainty analysis for explosive mixture components determination using multi-objective micro-genetic algorithm

In practical applications, many suspicious samples may be a kind of mixture and consist of various chemical components that make the spectral analysis difficult. Various explosives and related compounds (ERC) in the mixture can be identified and the concentration of each component can be estimated based on the known spectral data of the pure explosive components. In this paper, the terahertz spectroscopic uncertainty analysis using a micro-GA has been proposed, in which the random assignment of alleles from parents to offspring is implied. An intelligent computation-based technical road-map is also provided for the analysis and optimisation of the terahertz spectroscopic combination analysis. A simulation with two given test cases for the ERC has been devised. The results of the simulation show that micro-GA and its derivatives have the potential applications in the fields of security, medicine and food industry to fast identify mixtures.     

Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.     

An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows

The vehicle routing problem with time windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of routes to deliver goods, using a fleet of identical vehicles with restricted capacity, to customers with service time windows. However, there are other objectives, and having a range of solutions representing the trade-offs between objectives is crucial for many applications. Although previous research has used evolutionary methods for solving this problem, it has rarely concentrated on the optimization of more than one objective, and hardly ever explicitly considered the diversity of solutions. This paper proposes and analyzes a novel multi-objective evolutionary algorithm, which incorporates methods for measuring the similarity of solutions, to solve the multi-objective problem. The algorithm is applied to a standard benchmark problem set, showing that when the similarity measure is used appropriately, the diversity and quality of solutions is higher than when it is not used, and the algorithm achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-objective optimizer.     

An elitist non-dominated sorting genetic algorithm enhanced with a neural network applied to the multi-objective optimization of a polysiloxane synthesis process

This paper presents an original software implementation of the elitist non-dominated sorting genetic algorithm (NSGA-II) applied and adapted to the multi-objective optimization of a polysiloxane synthesis process. An optimized feed-forward neural network, modeling the variation in time of the main parameters of the process, was used to calculate the vectorial objective function of NSGA-II, as an enhancement to the multi-objective optimization procedure. An original technique was utilized in order to find the most appropriate parameters for maximizing the performance of NSGA-II. The algorithm provided the optimum reaction conditions (reaction temperature, reaction time, amount of catalyst, and amount of co-catalyst), which maximize the reaction conversion and minimize the difference between the obtained viscometric molecular weight and the desired molecular weight. The algorithm has proven to be able to find the entire non-dominated Pareto front and to quickly evolve optimal solutions as an acceptable compromise between objectives competing with each other. The use of the neural network makes it also suitable to the multi-objective optimization of processes for which the amount of knowledge is limited.     

基于参考点和投票规则的多目标粒子群优化群体决策

针对巨量可选方案的群体决策问题,提出了一个新的基于参考点和投票规则的多目标粒子群优化算法。该算法把个体与参考点的支配关系或者距离作为一个重要因素,在选择引导者的锦标赛方法,局部最优更新规则,以及外部种群档案剪枝规则中都嵌入了基于支配关系或距离因素的投票规则,以找到群体决策解,并且提高搜索效率。仿真结果表明该算法有效。     

A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems

This paper presents a novel discrete differential evolution (DDE) algorithm for solving the no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, the individuals in the DDE algorithm are represented as discrete job permutations, and new mutation and crossover operators are developed based on this representation. Second, an elaborate one-to-one selection operator is designed by taking into account the domination status of a trial individual with its counterpart target individual as well as an archive set of the non-dominated solutions found so far. Third, a simple but effective local search algorithm is developed to incorporate into the DDE algorithm to stress the balance between global exploration and local exploitation. In addition, to improve the efficiency of the scheduling algorithm, several speed-up methods are devised to evaluate a job permutation and its whole insert neighborhood as well as to decide the domination status of a solution with the archive set. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed DDE algorithm is superior to a recently published hybrid differential evolution (HDE) algorithm [Qian B, Wang L, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Computers & Operations Research 2009;36(1):209–33] and the well-known multi-objective genetic local search algorithm (IMMOGLS2) [Ishibuchi H, Yoshida I, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 2003;7(2):204–23] in terms of searching quality, diversity level, robustness and efficiency. Moreover, the effectiveness of incorporating the local search into the DDE algorithm is also investigated.     

Aircraft design optimization

In this paper we describe briefly a set of procedures for the optimal design of full mission aerospace systems. This involves multi-physics simulations at various fidelity levels, surrogates, distributed computing and multi-objective optimization. Low-fidelity analysis is used to populate a database of inputs and outputs of the system simulation and Neural Networks are then designed to generate inexpensive surrogates. Higher fidelity is used only where is warranted and also to do a local exploration after global optimization techniques have been used on the surrogates in order to provide plausible initial values. The ideas are exemplified on a generic supersonic aircraft configuration, where one of the main goals is to reduce the ground sonic boom.     

Reliability versus performance for critical applications

Applications implemented on critical systems are subject to both safety critical and real-time constraints. Classically, applications are specified as precedence task graphs that must be scheduled onto a given target multiprocessor heterogeneous architecture. We propose a new method for simultaneously optimizing two objectives: the execution time and the reliability of the schedule. The problem is decomposed into two successive steps: a spatial allocation during which the reliability is maximized (randomized algorithm), and a scheduling during which the makespan is minimized (list scheduling algorithm). It allows us to produce several trade-off solutions, among which the user can choose the solution that best fits the application’s requirements. Reliability is increased by replicating adequate tasks onto well chosen processors. Our fault model assumes that processors are fail-silent, that they are subject to transient failures, and that the occurrences of failures follow a constant parameter Poisson law. We assess and validate our method by running extensive simulations on both random graphs and actual application graphs. They show that it is competitive, in terms of makespan, compared to existing reference scheduling methods for heterogeneous processors (HEFT), while providing a better reliability.     

A robust scheduling method based on a multi-objective immune algorithm

A robust scheduling method is proposed to solve uncertain scheduling problems. An uncertain scheduling problem is modeled by a set of workflow models, and then a scheduling scheme (solution) of the problem can be evaluated by workflow simulations executed with the workflow models in the set. A multi-objective immune algorithm is presented to find Pareto optimal robust scheduling schemes that have good performance for each model in the set. The two optimization objectives for scheduling schemes are the indices of the optimality and robustness of the scheduling results. An antibody represents a resource allocation scheme, and the methods of antibody coding and decoding are designed to deal with resource conflicts during workflow simulations. Experimental tests show that the proposed method can generate a robust scheduling scheme that is insensitive to uncertain scheduling environments.     

Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H gate and R&N gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H gate and R&N gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified.