Multi‐objective memetic algorithm: comparing artificial neural networks and pattern search filter method approaches

In this work, two methodologies to reduce the computation time of expensive multi‐objective optimization problems are compared. These methodologies consist of the hybridization of a multi‐objective evolutionary algorithm (MOEA) with local search procedures. First, an inverse artificial neural network proposed previously, consisting of mapping the decision variables into the multiple objectives to be optimized in order to generate improved solutions on certain generations of the MOEA, is presented. Second, a new approach based on a pattern search filter method is proposed in order to perform a local search around certain solutions selected previously from the Pareto frontier. The results obtained, by the application of both methodologies to difficult test problems, indicate a good performance of the approaches proposed.     

Multi‐objective optimal motion control of a laboratory helicopter based on parallel simple cell mapping method

This paper presents a study of multi‐objective optimal design of nonlinear control systems and has validated the control design with a twin rotor model helicopter. The gains of the porportional integral differential (PID) control are designed in the framework of multi‐objective opitmization. Eight design paramaters are optimized to minimize six time‐domain objective objective functions. The study of multi‐objective optimal design of feedback control with such a number of design paramaters and objective functions is rare in the literature. The Pareto optimal solutions are obtained by the proposed parallel simple cell mapping method consisting of a robust Pareto set recovery algorithm and a rolling subdivision technique. The proposed parallel simple cell mapping algorithm has two features: the number of cells in the invariant set grows linearly with the rolling subdivisions, and the Pareto set is insensitive to the inital set of seed cells. The current control design is compared with the classical LQE control for linear systems, and is also experimentally validated. The current design provides improved control performance as compares with the LQR control, and is applicable to complex nonlinear systems.     

Evolutionary algorithms for the multi‐objective test data generation problem

Automatic test data generation is a very popular domain in the field of search‐based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behavior. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense by considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we did in this paper. We mainly compared two approaches to deal with the multi‐objective test data generation problem: a direct multi‐objective approach and a combination of a mono‐objective algorithm together with multi‐objective test case selection optimization. Concretely, in this work, we used four state‐of‐the‐art multi‐objective algorithms and two mono‐objective evolutionary algorithms followed by a multi‐objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed of 800 Java programs created through a program generator. The second benchmark is composed of 13 real programs extracted from the literature. In the direct multi‐objective approach, the results indicate that the oracle cost can be properly optimized; however, the full branch coverage of the system poses a great challenge. Regarding the mono‐objective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective in maximizing the branch coverage. Copyright © 2011 John Wiley & Sons, Ltd.     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

Pareto‐Set Based Multi‐Objective Dynamic Reactive Power and Voltage Control

A novel approach is proposed in this paper to solve multi‐objective dynamic reactive power and voltage control (Volt/VAR control, VVC). The method is able to attain the Pareto‐optimal solutions, based on the day‐ahead load forecast, for the VVC considering reducing daily power loss, enhancing voltage profile and optimizing dispatch schedules for on‐load tap changer (OLTC) and shunt capacitor switching, which will provide decision maker more options to schedule the dynamic VVC. This approach is simulated in IEEE14 buses system and IEEE30 buses system, and the results are encouraging with respect to performance in dynamic reactive power control. Moreover, the application in an actual distribution system verifies its effectiveness further.     

An iteration‐based hybrid parallel algorithm for tridiagonal systems of equations on multi‐core architectures

An optimized parallel algorithm is proposed to solve the problem occurred in the process of complicated backward substitution of cyclic reduction during solving tridiagonal linear systems. Adopting a hybrid parallel model, this algorithm combines the cyclic reduction method and the partition method. This hybrid algorithm has simple backward substitution on parallel computers comparing with the cyclic reduction method. In this paper, the operation count and execution time are obtained to evaluate and make comparison for these methods. On the basis of results of these measured parameters, the hybrid algorithm using the hybrid approach with a multi‐threading implementation achieves better efficiency than the other parallel methods, that is, the cyclic reduction and the partition methods. In particular, the approach involved in this paper has the least scalar operation count and the shortest execution time on a multi‐core computer when the size of equations meets some dimension threshold. The hybrid parallel algorithm improves the performance of the cyclic reduction and partition methods by 19.2% and 13.2%, respectively. In addition, by comparing the single‐iteration and multi‐iteration hybrid parallel algorithms, it is found that increasing iteration steps of the cyclic reduction method does not affect the performance of the hybrid parallel algorithm very much. Copyright © 2015 John Wiley & Sons, Ltd.     

协同供应链多级库存控制的多目标优化模型及其求解方法

在多级库存的协调控制过程中，只考虑成本的单目标优化模型对于提高供应链总体性能水平存在着局限，本文提出了考虑需求满足率、时间、成本的多目标协同优化模型，对于多品种、复杂拓扑结构，以及库容、生产能力受限的情况，提出了一种在外层对库存策略和内层对物流分配方案分别进行寻优的双层求解方法，并采用演化多目标优化技术构造了算法. 通过算例实验对模型的有效性进行了验证，实验结果表明，基于多目标模型的优化结果使得系统总体性能得到显著改善.     

Solving scalarized multi‐objective network flow problems using an interior point method

In this paper, we present a primal‐dual interior‐point algorithm to solve a class of multi‐objective network flow problems. More precisely, our algorithm is an extension of the single‐objective primal infeasible dual feasible inexact interior point method for multi‐objective linear network flow problems. Our algorithm is contrasted with standard interior point methods and experimental results on bi‐objective instances are reported. The multi‐objective instances are converted into single objective problems with the aid of an achievement function, which is particularly adequate for interactive decision‐making methods.     

An evolutionary algorithm for the multi‐objective pick‐up and delivery pollution‐routing problem

The design of sustainable logistics solutions poses new challenges for the study of vehicle‐routing problems. The design of efficient systems for transporting products via a heterogeneous fleet of vehicles must consider the minimization of cost, emissions of greenhouse gases, and the ability to serve every customer within an available time slot. This phenomenon gives rise to a multi‐objective problem that considers the emission of greenhouse gases, the total traveling time, and the number of customers served. The proposed model is approached with an ε‐constraint technique that allows small instances to be solved and an evolutionary algorithm is proposed to deal with complex instances. Results for small instances show that all the points that approach the Pareto frontier found by the evolutionary algorithm are nondominated by any solution found by the multi‐objective model. For complex instances, nondominated solutions that serve most of the requests are found with low computational requirements.     

Multi‐objective optimization using metaheuristics: non‐standard algorithms

In recent years, the application of metaheuristic techniques to solve multi‐objective optimization problems has become an active research area. Solving this kind of problems involves obtaining a set of Pareto‐optimal solutions in such a way that the corresponding Pareto front fulfils the requirements of convergence to the true Pareto front and uniform diversity. Most of the studies on metaheuristics for multi‐objective optimization are focused on Evolutionary Algorithms, and some of the state‐of‐the‐art techniques belong this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi‐objective optimization. In particular, we focus on non‐evolutionary metaheuristics, hybrid multi‐objective metaheuristics, parallel multi‐objective optimization and multi‐objective optimization under uncertainty. We analyze these issues and discuss open research lines.     

Engineering an efficient two‐phase local search algorithm for the co‐rotating twin‐screw extruder configuration problem

The twin‐screw configuration problem arises during polymer extrusion and compounding. It consists in defining the location of a set of pre‐defined screw elements along the screw axis in order to optimize different, typically conflicting objectives. In this paper, we present a simple yet effective stochastic local search (SLS) algorithm for this problem. Our algorithm is based on efficient single‐objective iterative improvement algorithms, which have been developed by studying different neighborhood structures, neighborhood search strategies, and neighborhood restrictions. These algorithms are embedded into a variation of the two‐phase local search framework to tackle various bi‐objective versions of this problem. An experimental comparison with a previously proposed multi‐objective evolutionary algorithm shows that a main advantage of our SLS algorithm is that it converges faster to a high‐quality approximation to the Pareto front.     

An efficient framework for parallel and continuous frequent item monitoring

In high‐speed network monitoring, the ever‐growing traffic calls for a high‐performance solution for the computation of frequent items. The increasing number of cores in the current commodity multi‐core processors opens up new opportunities in parallelization. In this paper, we present a novel precision integrated framework (PRIF) that exploits the great parallel capability of multi‐cores to speed up the famous frequent algorithm. PRIF equally distributes the input data stream into sub‐threads that use the optimized weighted frequent algorithm to track local frequent items. The items with frequency increments exceeding a pre‐defined threshold are sent to a merging thread which is able to return the global continuous ε‐deficient frequent items. The theoretical correctness and complexity analyses are presented. Experiments with real and synthetic traces confirm the theoretical analyses and demonstrate the excellent performance as well as the effects of parameters and data skewness. The results show that PRIF is able to provide continuous frequent items and near‐linear speedup at the cost of greater memory use. Copyright © 2013 John Wiley & Sons, Ltd.     

Facility layout design using a multi‐objective interactive genetic algorithm to support the DM

The unequal area facility layout problem (UA‐FLP) has been addressed by many methods. Most of them only take aspects that can be quantified into account. This contribution presents a novel approach, which considers both quantitative aspects and subjective features. To this end, a multi‐objective interactive genetic algorithm is proposed with the aim of allowing interaction between the algorithm and the human expert designer, normally called the decision maker (DM) in the field of UA‐FLP. The contribution of the DM's knowledge into the approach guides the complex search process, adjusting it to the DM's preferences. The entire population associated to facility layout designs is evaluated by quantitative criteria in combination with an assessment prepared by the DM, who gives a subjective evaluation for a set of representative individuals of the population in each iteration. In order to choose these individuals, a soft computing clustering method is used. Two interesting real‐world data sets are analysed to empirically probe the robustness of these models. The first UA‐FLP case study describes an ovine slaughterhouse plant and the second, a design for recycling carton plant. Relevant results are obtained, and interesting conclusions are drawn from the application of this novel intelligent framework.     

A single front genetic algorithm for parallel multi-objective optimization in dynamic environments

This paper proposes a new parallel evolutionary procedure to solve multi-objective dynamic optimization problems along with some measures to evaluate multi-objective optimization in dynamic environments. These dynamic optimization problems appear in quite different real-world applications with actual socio-economic relevance. In these applications, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online whilst the size of the changes is unknown. Although parallel processing could be very useful in these problems to meet the solution quality requirements and constraints, to date, not many parallel approaches have been reported in the literature. Taking this into account, we introduce a multi-objective optimization procedure for dynamic problems that are based on PSFGA, a parallel evolutionary algorithm previously proposed by us for multi-objective optimization. It uses an island model where a process divides the population among the remaining processes and allows the communication and coordination among the subpopulations in the different islands. The proposed algorithm makes an exclusive use of non-dominating individuals for the selection and variation operator and applies a crowding mechanism to maintain the diversity and the distribution of the solutions in the Pareto front. We also propose a model to understand the benefits of parallel processing in multi-objective problems and the speedup figures obtained in our experiments.     

Enhanced genetic algorithms for a bi‐objective bus driver rostering problem: a computational study

In this work, the bus driver rostering problem is considered in the context of a noncyclic rostering, with two objectives representing either the company or the drivers’ interests. A network model and a proof of the NP‐hardness of the problem are presented, along with a bi‐objective memetic algorithm that combines a specific decoder with a utopian/lexicographic elitism, a strength Pareto fitness evaluation, and a local search procedure. By taking real and benchmark instances the computational behavior of the memetic algorithm is compared with simpler versions to assess the effects of the embedded components. The developed algorithm is a valuable tool for bus companies’ planning departments insofar as it yields at low computing times a pool of good quality rosters that reconcile contradictory objectives. This study shows that simple enhancements in standard bi‐objective genetic algorithms may improve the results for this difficult combinatorial problem.     

解约束多目标优化问题的一种鲁棒的进化算法

将约束条件与目标函数融合在一起，对有约束的多目标优化问题(MOP)建立了一种新的偏序关系，引入了约束占优的定义，并证明了在新的偏序关系意义下的Pareto最优集就是满足约束条件的Pareto最优集，从而在对种群中的个体进行评估或排序时，并不需要特别去关心个体是否可行，避免了罚函数选择参数的困难，尝试应用有限Markov链的有关理论证明了此进化算法的收敛性，用较复杂的Benchmark函数进行了大量的数值实验，测试结果表明新算法在解集分布的均匀性、多样性以及快速收敛性均较理想。     

Multi‐core scalable and efficient pathfinding with Parallel Ripple Search

Game developers are often faced with very demanding requirements on huge numbers of agents moving naturally through increasingly large and detailed virtual worlds. With the advent of multi‐core architectures, new approaches to accelerate expensive pathfinding operations are worth being investigated. Traditional single‐processor pathfinding strategies, such as A^* and its derivatives, have been long praised for their flexibility. We implemented several parallel versions of such algorithms to analyze their intrinsic behavior, concluding that they have a large overhead, yield far from optimal paths, do not scale up to many cores or are cache unfriendly. In this article, we propose Parallel Ripple Search, a novel parallel pathfinding algorithm that largely solves these limitations. It utilizes a high‐level graph to assign local search areas to CPU cores at “equidistant” intervals. These cores then use A^* flooding behavior to expand towards each other, yielding good “guesstimate points” at border touch on. The process does not rely on expensive parallel programming synchronization locks but instead relies on the opportunistic use of node collisions among cooperating cores, exploiting the multi‐core's shared memory architecture. As a result, all cores effectively run at full speed until enough way‐points are found. We show that this approach is a fast, practical and scalable solution and that it flexibly handles dynamic obstacles in a natural way. Copyright © 2012 John Wiley & Sons, Ltd.     

EVOLUTIONARY SEARCH FOR LIMIT CYCLE AND CONTROLLER DESIGN IN MULTIVARIABLE NONLINEAR SYSTEMS

A feature of many practical control systems is a Multi‐Input Multi‐Output (MIMO) interactive structure with one or more gross nonlinearities. A primary controller design task in such circumstances is to predict and ensure the avoidance of limit cycling conditions followed by achieving other design objectives. This paper outlines how such a system may be investigated using the Sinusoidal Input Describing Function (SIDF) philosophy quantifying magnitude, frequency and phase of any possible limit cycle operation. While Sinusoidal Input Describing function is a suitable linearization technique in the frequency domain for assessment of stability and limit cycle operation, it can not be employed in time domain. In order to be able to incorporate the time domain requirements in an overall controller design technique, the appropriate linearization technique suggested here is the Exponential Input Describing Function (EIDF). First, an evolutionary search based on a multi‐objective formulation is employed for the direct solution of the harmonic balance system matrix equation. The search is based on Multi‐Objective Genetic Algorithms (MOGA) and is capable of predicting specified modes of theoretically possible limit cycle operation. Second, the design requirements in time as well as frequency domain are formulated by a set of constraint inequalities. A numerical synthesis procedure also based on Multi‐Objective Genetic Algorithm is employed to adjust the initial compensator parameters to meet the imposed constraints. Robust stability and robust performance are investigated with respect to linearization uncertainty within the context of multiobjective formulation. In order to make the Genetic Algorithm (GA) search more amenable to design trade‐off between different and often contradictory specifications, a weighted sum of the functions is introduced. This criterion is subsequently optimized subject to the nonlinear system dynamics and a set of design requirements. Examples of use are given to illustrate the effectiveness of the proposed approach.     

Search for phosphors for use in displays and lighting using heuristics‐based combinatorial materials science

Abstract— According to the recent demand for materials for use in various displays and solid‐state lighting, new phosphors with improved performance have been consistently pursued. Multi‐objective genetic‐algorithm‐assisted combinatorial‐material‐search (MOGACMS) strategies have been applied to various multi‐compositional inorganic systems to search for new phosphors and to optimize the properties of phosphors. In addition, the troublesome, complex problem of high‐throughput experimentation (HTE), the inconsistency, which is frequently faced by combinatorial material scientists, is especially emphasized. The luminance and inconsistency was treated as two objective functions in our MOGACMS strategy to pinpoint and optimize promising phosphors with high photoluminance and reliable reproducibility. Using MOGACMS, several multi‐dimensional oxide systems were screened in term of the minimization of inconsistency and the maximization of luminance.     

基于动态调整的多目标粒子群优化算法

为了改善多目标粒子群优化算法生成的最终Pareto前端的多样性和收敛性,提出了一种针对多目标粒子群算法进化状态的检测机制.通过对外部Pareto解集的更新情况进行检测,进而评估算法的进化状态,获取反馈信息来动态调整进化策略,使得算法在进化过程中兼顾近似Pareto前端的多样性和收敛性.最后,在ZDT系列测试函数中,将本文算法与其他4种对等算法比较,证明了本文算法生成的最终Pareto前端在多样性和收敛性上均有显著的优势.