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With the ubiquitous of GPS-equipped devices, spatial crowdsourcing (SC) technology has been widely utilized in our daily life. As a novel computing paradigm, it hires mobile users as workers who physically move to the location of the task and perform the task. Task assignment is a fundamental issue in SC. In real life, there are many complex tasks requiring different workers, among which the quality of worker cooperation and the price satisfaction of users should not be ignored. Hence, this paper examines a satisfaction-aware task assignment (SATA) problem with the goal of maximizing overall user satisfaction, where the user satisfaction integrates the satisfaction towards price and cooperation quality. The SATA problem has been proved to be NP-hard by reducing it from the k -set packing problem. In addition, two algorithms, namely, conflict-aware greedy (CAG) algorithm and game theoretic (GT) algorithm with an optimization strategy, are proposed for solving the SATA problem. The CAG algorithm can efficiently obtain a result with provable approximate bound, while the GT algorithm is proven to be convergent which can find a Nash equilibrium. Extensive experiments have demonstrated the effectiveness and efficiency of our proposed approaches on both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

With the ubiquitous of GPS-equipped devices, spatial crowdsourcing (SC) technology has been widely utilized in our daily life. As a novel computing paradigm, it hires mobile users as workers who physically move to the location of the task and perform the task. Task assignment is a fundamental issue in SC. In real life, there are many complex tasks requiring different workers, among which the quality of worker cooperation and the price satisfaction of users should not be ignored. Hence, this paper examines a satisfaction-aware task assignment (SATA) problem with the goal of maximizing overall user satisfaction, where the user satisfaction integrates the satisfaction towards price and cooperation quality. The SATA problem has been proved to be NP-hard by reducing it from the k -set packing problem. In addition, two algorithms, namely, conflict-aware greedy (CAG) algorithm and game theoretic (GT) algorithm with an optimization strategy, are proposed for solving the SATA problem. The CAG algorithm can efficiently obtain a result with provable approximate bound, while the GT algorithm is proven to be convergent which can find a Nash equilibrium. Extensive experiments have demonstrated the effectiveness and efficiency of our proposed approaches on both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

The maximal flow problems (MFPs) are among the most significant optimization problems in network flow theory with widespread and diverse applications. To represent qualitative aspects of uncertainty in the maximal flow model, which asks for the largest amount of flow transported from one vertex to another, the use of linguistic variables has effective means for experts in expressing their views. In this paper, we first define trapezoidal Pythagorean fuzzy numbers (TrPFNs) along with some new arithmetic operations which cover the gaps in previously defined operations. For defuzzification of TrPFNs, we introduce a ranking procedure based on value and ambiguity indices. This work puts forward a theoretical framework for a new Pythagorean fuzzy maximal flow algorithm (PFMFA), which helps to solve different optimization problems with PF information by considering linguistic capacities and flows. The implementation of the algorithm is elaborated by considering two case studies. Firstly, we examine the maximum flow of a water distribution pipeline network in Pyigyitagon Township, Mandalay, Myanmar. Secondly, we compute maximum PF power flow in a 14-bus electricity network provided by the IEEE working group, concerning the example data from the University of Washington. The results illustrate the superiority of the proposed method and give a detailed analysis of flow connected with several practical performances. In addition, the Pythagorean fuzzy optimal flows corresponding to each network arc are compared and performance comparison of our method is investigated which shows the increasing and decreasing trends of backward and forward arcs of the network, respectively. Moreover, the runtime analysis of existing well-known maximal flow algorithms is provided. Finally, we present the advantages of our technique to promote its cogency. [ABSTRACT FROM AUTHOR]

[Display omitted] • A two-phase optimization system for solving classification problems. • Combining Auto-ML pipelines to improve overall performance. • Intelligent selection of ensemble methods. • Best results delivered by double-fault measure and 20 or 50 maximum number of base models. Automatic Machine Learning (Auto-ML) tools enable the automatic solution of real-world problems through machine learning techniques. These tools tend to be more time consuming than standard machine learning libraries, therefore, exploiting all the available resources to the full is a valuable feature. This paper presents a two-phase optimization system for solving classification problems. The system is designed to produce more robust classifiers by exploiting the different architectures that are generated while solving classification problems with Auto-ML tools, particularly AutoGOAL. In the first phase, the system follows a probabilistic strategy to find the best combination of algorithms and hyperparameters to generate a collection of base models according to certain diversity criteria; and in the second, it follows similar Auto-ML strategies to ensemble those models. The HAHA 2019 challenge corpus and the Adult dataset were used to evaluate the system. The experimental results show that: i) a better solution can be built by ensembling a subset of the already tested models; ii) the performance of ensemble methods depends on the collection of base models used; and, iii) ensuring diversity using the double-fault measure produces better results than the disagreement measure. The source code is available online for the research community. [ABSTRACT FROM AUTHOR]

Abstract Metaheuristic algorithms usually aim to solve nonconvex optimization problems in black-box and high-dimensional scenarios. Characterizing and understanding the properties of nonconvex problems is therefore important for effectively analyzing metaheuristic algorithms and their development, improvement and selection for problem solving. This paper establishes a novel analysis framework called nonconvex ratio analysis , which can characterize nonconvex continuous functions by measuring the degree of nonconvexity of a problem. This analysis uses two quantitative measures: the nonconvex ratio for global characterization and the local nonconvex ratio for detailed characterization. Midpoint convexity and Monte Carlo integral are important methods for constructing the measures. Furthermore, as a practical feature, we suggest a rapid characterization measure that uses the local nonconvex ratio and can characterize certain black-box high-dimensional functions using a much smaller sample. Throughout this paper, the effectiveness of the proposed measures is confirmed by numerical experiments using the COCO function set. [ABSTRACT FROM AUTHOR]

This paper proposes a particle swarm optimization with strategy dynamics (SDPSO) to solve single-objective optimization problems. SDPSO consists of four PSO search strategies. Evolutionary game theory is introduced to control the population state. In evolutionary game theory, through the interaction between players, better strategies will eventually dominate among the players. By extending this idea to PSO, a selection mechanism and a mutation mechanism are proposed. By using the selection mechanism, the adoption probability of the high payoff strategies will increase. The mutation mechanism can examine the stability of the incumbent strategy to evolutionary pressures. The performance of SDPSO is compared with 14 algorithms on the CEC 2014 test suite. The results show that SDPSO has the highest rank. SDPSO is applied to solve a real-world problem. SDPSO can find the best mean results comparing with 4 algorithms. The findings show that the proposed evolutionary game theory-based framework can adaptively control the population state. This study proposes a new application of evolutionary game theory to the design of swarm intelligence and contributes to a better understanding of the usefulness of the evolutionary game theory in the optimization method. The source codes of SDPSO are available at https://github.com/zi-ang-liu/SDPSO. [ABSTRACT FROM AUTHOR]

• A supervised multi-view model is proposed to realize a block-based feature selection. • The proposed model is composed of all sharing sub-models in each class. • The sparse regularizer can enhance the sparsity of blocks from features and views. • The proposed algorithm can realize the block separation and independent solution. • Numerical experiments show the effectiveness of our method on large-scale datasets. With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view's locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed. [ABSTRACT FROM AUTHOR]

• A hybridization approach for improving population-based meta-heuristics is proposed. • The worst candidates in a population are replaced by predicted candidates. • The calibration problem based on the population is solved to create candidates. • The hybridized ten meta-heuristics outperform their counterparts. The recent proliferation of population-based meta-heuristics designed for solving optimization problems and their successes confirm that more promising techniques inspired by physical phenomena or biological systems are desired. Therefore, in this paper, a novel hybridization approach is proposed to improve the performance of optimization algorithms. The approach replaces a small number of worst solutions obtained by a meta-heuristic with predicted candidates without altering its search operators. Specifically, a target fitness value of the predicted candidate is determined based on the fitness of the population and a search strategy. Then, a calibration problem is solved to infer its decision variables. In this study, the proposed hybridization technique is applied to ten state-of-the-art population-based algorithms. The meta-heuristics and hybrids are evaluated on 82 functions, four engineering problems, and a new challenging problem of estimating a constant in Markov's inequality using minimal polynomials of different degrees. The experimental results reveal the superiority of the hybrids over their counterparts and confirm the suitability of the proposed approach for improving the efficiency of meta-heuristics. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to propose an effective evolutionary method for solving the problem of working paths optimization in survivable MPLS network. The paper focuses on existing network, in which only network flow can be optimized to provide network survivability using the local repair strategy. The problem is NP-complete, the solution space of the test cases is large and many genes are required to code the potential solution. Recently, the MuPPetS method (Multi-Population Pattern Searching Algorithm for Flow Assignment) was proposed and seems to be a promising tool for tackling high-dimensional, hard optimization problems. The MuPPetS is a linkage learning method that minimizes the negative effects of typical EA bottlenecks, e.g., preconvergence and significant effectiveness dropdown caused by an increasing number of genes in the chromosome. In comparison to other evolutionary methods, the MuPPetS was shown to be effective and capable of solving GA-hard problems. Therefore, the proposed MuPPetS-FuN method (Multi-Population Pattern Searching Algorithm for Flow Assignment in Non-bifurcated Commodity Flow) is based on MuPPetS. The additional objective of this paper is to propose changes to general MuPPetS framework to increase its effectiveness via better subpopulation number control strategy. [ABSTRACT FROM AUTHOR]

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed. In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice’s (1976) selection framework. We describe each of the four component spaces – problem, algorithm, performance and characteristic – in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions. [ABSTRACT FROM AUTHOR]

Three major sources of complexity in many real-world problems are size, variable interaction, and interdependence of the subcomponents of a problem. With the rapid growth in the size of businesses, the demand for solving large-scale complex problems will continue to grow. In this paper, we propose several major design features that need to be incorporated into large-scale optimization benchmark suites in order to better resemble the features of real-world problems. Non-uniform subcomponent sizes, imbalance between the contribution of various subcomponents of a problem, and the interaction between subcomponents by means of overlapping subcomponents are among these features. The proposed features are designed with the aim of closing the gap between the theory and practice of evolutionary techniques for solving large-scale continuous optimization problems. The general guidelines proposed in this paper can be used to design and construct various benchmark suites to meet different needs. The IEEE CEC’2013 large-scale global optimization benchmark suite [29] is one such implementation. The paper also contains a brief discussion on how the CEC’2013 benchmarks can be extended or modified for various purposes. Finally, a preliminary comparative study is conducted to showcase the performance of several state-of-the-art algorithms on the CEC’2013 large-scale benchmark problems. [ABSTRACT FROM AUTHOR]

Novel Evolutionary Algorithms are usually tested on sets of artificially-constructed benchmark problems. Such problems are often created to make the search of one global extremum (usually minimum) tricky. In this paper it is shown that benchmarking heuristics on either minimization or maximization of the same set of artificially-created functions (with equal bounds and number of allowed function calls) may lead to very different ranking of tested algorithms. As Evolutionary Algorithms and other heuristic optimizers are developed in order to be applicable to real-world problems, such result may raise doubts on the practical meaning of benchmarking them on artificial functions, as there is little reason that searching for the minimum of such functions should be more important than searching for their maximum. Thirty optimization heuristics, including a number of variants of Differential Evolution, as well as other kinds of Evolutionary Algorithms, Particle Swarm Optimization, Direct Search methods and – following the idea borrowed from No Free Lunch – pure random search are tested in the paper. Some discussion regarding the choice of the mean or the median performance for comparison is addressed and a short debate on the overall performance of particular methods is given. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a linear programming technique for multidimensional analysis of preference (LINMAP) to address multiple criteria decision analysis problems within the interval type-2 fuzzy environment based on interval type-2 trapezoidal fuzzy numbers. Considering the issue of anchor dependency, we use multiple anchor points in the decision-making process and employ approximate positive-ideal and negative-ideal solutions as the points of reference. Selected useful properties of the approximate ideal solutions are also investigated. In contrast to the classical LINMAP methods, this paper directly generates approximate ideal solutions from the characteristics of all alternatives. Next, this work presents the concept of closeness-based indices using Minkowski distances with approximate ideal solutions to develop a new approach for determining measurements of consistency and inconsistency. Under incomplete preference information on paired comparisons of the alternatives, this paper provides a novel method that uses the concept of comprehensive closeness-based indices to measure the poorness of fit and the goodness of fit. By applying the consistency indices and inconsistency indices, this work formulates an optimization problem that can be solved for the optimal weights of the criteria and thus acquires the best compromise alternative. Additionally, this paper explores the problem of supplier selection and conducts a comparative discussion to validate the effectiveness and applicability of the proposed interval type-2 fuzzy LINMAP method with approximate ideal solutions. Furthermore, the proposed method is applied to address a marketplace decision difficulty (MPDD)-prone decision-making problem to provide additional contributions for practical implications. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are extensively recognized as effective approaches for solving high-dimensional optimization problems. These algorithms provide effective tools with important applications in business, engineering, economics, and science. This paper surveys state-of-the-art metaheuristic algorithms and their current applications in the field of large-scale global optimization. The paper mainly covers the fundamental algorithmic frameworks such as decomposition and non-decomposition methods. More than 200 papers are carefully reviewed to prepare the current comprehensive survey. [ABSTRACT FROM AUTHOR]

Intuitively, the main novelties of the proposed Tk-MaOEA can be presented as follows. • In the global space optimization level, a new transfer learning approach is developed to reduce a large number of objectives while the original property of the problem is kept well. The proposed approach uses a specific transfer matrix to compress the search space, which is simple yet effective to handle with the curse of dimensionality in MaOPs. • In the objective optimization level, the Kriging model is devised for each objective to further reduce its computational cost. This Bayesian based surrogate model is to measure not only the objective value itself but also stochastic error of the approximation, which is essentially conductive to improve the accuracy of the optimization. • The multi-scale normalization approach is employed so as to avoid the distortion caused by conventional normalization in the high-dimensional objective space. This is a significant operation for the MaOEA to keep unchanged spatial distribution when the original population are normalized. • The FCS approach is incorporated instead of the traditional crowding distance method in the environmental selection. This approach is more effective to select a set of representative non-dominated solutions in a single run. Due to the curse of dimensionality caused by the increasing number of objectives, it is very challenging to tackle many-objective optimization problems (MaOPs). Aiming at this issue, this paper proposes a novel many-objective evolutionary algorithm, called Tk-MaOEA, based on transfer matrix assisted by Kriging model. In this approach, for the global space optimization, a transfer matrix is used as a map tool to reduce the number of objectives, which can simplify the optimization process. For the objective optimization, the Kriging model is incorporated to further reduce the computation cost. In addition, the fast non-dominated sorting and farthest-candidate selection (FCS) methods are used to guarantee the diversity of solutions. Comprehensive experiments on a set of benchmark functions have been conducted. Experimental results show that Tk-MaOEA is effective for solving complex MaOPs. [ABSTRACT FROM AUTHOR]

Compared with domination-based methods, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is less prone to the difficulty caused by an increase in the number of objectives. It is a promising algorithmic framework for solving many-objective optimization problems (MaOPs). In MOEA/D, the target MaOP is decomposed into a set of single-objective problems by using a scalarizing function with evenly specified weight vectors. Among the available scalarizing functions, penalty-based boundary intersection (PBI) with an appropriate penalty parameter is known to perform well. However, its performance is heavily influenced by the setting of the penalty factor (θ), which can take a value from zero to +∞. A limited amount of work has thus far considered the choice of an appropriate value of θ. This paper presents a comprehensive experimental study on WFG and WFG-extend problems featuring two to 15 objectives. A range of values of θ is investigated to understand its influence on the performance of the PBI-based MOEA/D (MOEA/D-PBI). Based on the observations, the range of values of θ are divided into three sub-regions, and a two-stage adaptive penalty scheme is proposed to adaptively choose an appropriate value from 0.001 to 8000 during an optimization run. The results of experiments show that, the robustness of MOEA/D-PBI can be significantly enhanced using the proposed scheme. [ABSTRACT FROM AUTHOR]

It is known that Pareto dominance encounters difficulties in many-objective optimization. This strict criterion could make most individuals of a population incomparable in a high-dimensional space. A straightforward approach to tackle this issue is modify the Pareto dominance criterion. This is typically done by relaxing the dominance region. However, this modification is often associated with one or more parameters of determining the relaxation degree, and the performance of the corresponding algorithm could be sensitive to such parameters. In this paper, we propose a new dominance criterion, angle dominance, to deal with many-objective optimization problems. This angle dominance criterion can provide sufficient selection pressure towards the Pareto front and be exempt from the parameter tuning. In addition, an interesting property of the proposed dominance criterion, in contrast to existing dominance criteria, lies in its capability to reflect an individual's extensity in the population. The angle dominance is integrated into NSGA-II (instead of Pareto dominance) and has demonstrated high competitiveness in many-objective optimization in comparison with a range of peer algorithms. [ABSTRACT FROM AUTHOR]

• The performance of three crossover operators of NSGA-III algorithm on big data optimization problems are investigated. • By introducing the concept of Stud, several improved crossover operators of SBX, UC, and SI have been designed. • Ten variants of NSGA-III algorithms are proposed. • The performance of ten NSGA-III variants are further verified on big data optimization problems. Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants. [ABSTRACT FROM AUTHOR]

• A new multi-layer interaction preference based multi-objective optimization algorithm through decomposition (MLIP-MOEA/D) is proposed. • A fast way to get preference region is established, during the selection process, the preference weight vector is redefined using the angle-based method. • A novel multi-layer interaction strategy for searching the accurate solutions is proposed. During evolution, the preferred region is reduced gradually, making it easier for user to get the final preferred solution(s). • Besides ZDT problems with two objectives, the proposed algorithm shows its potentiality for solving many-objective problems, i.e., DTLZ problems with 3, 5, 8, 10 objectives. Many problems in real world have not only one objective to be met. In the majority of cases, a set of trade-off solutions which spread evenly along the entire Pareto optimal front are generated by multi-objective evolutionary algorithms (MOEAs). Taking the preference of decision maker (DM) into consideration, some specified solutions can be obtained, which is of great interest in practical applications. In this paper, a novel multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition (denoted as MLIP-MOEA/D) is proposed. In MLIP-MOEA/D, a multi-layer interactive strategy is developed during evolution, in the first-layer interaction, the DM will provide a reference vector and an initial radius to determine a preference range, then all solutions in this range will be updated. The algorithm will stop if the DM is satisfied with the first output result, otherwise it will go on to the second-layer interaction. In this step, the most preferred solution generated from the first-layer interaction will be chosen as the new preference direction, and the weight vector is redefined by the angle-based method, and the range of preferred region is reduced gradually, until the closest solution that meet the DM's need is found. The algorithm is tested on a set of benchmark problems including DTLZ problems with more than three objectives, the experimental studies show that the proposed algorithm can effectively search the preferred solutions with the preference information and successfully deal with many-objective optimization problems. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms based on decomposition (MOEA/D) decomposes a multi-objective optimization problem (MOP) into a set of simple scalar objective optimization sub-problems and solves them in a collaborative way. Since the sub-problems are different in optimization difficulty and computational resource demanding, it is critical to reasonably allocate computational resources among them, which can optimize the usage of resources and improve the performance of an algorithm. This paper proposes a new resource allocation strategy based on the relationship between sub-problems for MOEA/D. A probability vector is maintained based on the relationship between sub-problems, which is used to guide the selection of sub-problems for optimization. In the optimization process, we explored the role of priority optimization of boundary sub-problems and used it to assist in the update of probability vector in the early optimization phase. A steady-state algorithm is designed and tested experimentally. The results suggest that the designed algorithms have some advantages over existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In evolutionary multi-objective optimization, it is crucial for the evolutionary algorithm to maintain a good balance between convergence and diversity. The recently proposed Two_Arch2 algorithm provides a new perspective to solve this problem. However, due to the properties of the mating mechanism and the limitations of the distance-based diversity maintenance scheme, both the computational complexity and the diversity face great challenges as the number of objectives increases. In this paper, we propose an improved Two-Archive algorithm for both multi- and many-objective optimization, aiming at further promoting the balance between convergence and diversity. In the proposed algorithm, we introduce a decomposition idea into the mating pool of the convergence archive, which increases the number of favorable solutions and reduces the computational complexity. At the same time, we apply a penalty angle-based selection scheme to the diversity archive, which effectively maintains the population diversity. The effectiveness of the proposed algorithm is compared with five state-of-the-art multi-objective evolutionary algorithms on a variety of benchmark problems. The experimental results demonstrate that the proposed algorithm has highly competitive performance on both multi- and many-objective optimization problems—in particular, remedying problems of Two_Arch2. • A decomposition-based two-archive algorithm is proposed to promote the balance between convergence and diversity in many-objective optimization. • A penalty angle-based selection scheme is proposed to maintain the diversity of the population. • The solutions in the diversity archive are merged to the convergence archive to achieve a more uniform distribution of the solutions. • Only two solutions are considered in the mating selection of each sub-region, which makes the behavior resemble that of a steady-state algorithm. [ABSTRACT FROM AUTHOR]

Abstract In this contribution we consider methods for improving the quality of classification by the k nearest neighborhood classifiers in the case of large number of missing values. The classical version of such classifier is applied, moreover the classifier with the aggregation of certainty coefficients of the individual classifiers with the use of the arithmetic mean, as well as new versions of the classifier with the use of diverse interval-valued aggregation functions are considered. After adding missing values to the data, the quality of the classical method decreases. By contrast, the interval modeling of missing values and application of interval-valued aggregation functions in the classifiers entail a much slower decrease of their quality. The paper includes the results of experiments performed on the data sets from the machine learning repository UCI. [ABSTRACT FROM AUTHOR]

Abstract Environmental changes are traditionally considered intrinsic in evolutionary dynamic optimization. However, by ignoring that changes can instead be induced, we are ignoring that environmental changes can be eventually beneficial. To investigate the impact of artificial changes on the optimization speed up, we propose a framework for inducing artificial changes in any pseudo-Boolean or continuous optimization in this paper. Seven types of changes can be induced. Knowing when and how the changes occur allows us to design new strategies for evolutionary algorithms. Through computational experiments and illustrative examples, the impact of introducing changes in the optimization process is investigated. Experimental results indicate that changing the environments according to the proposed framework can lead to higher speed up, but not for all problems and change types. The best performance was obtained by change types that introduce plateaus and/or modify the gradient of regions of the fitness landscape around the current best solution. By doing this, the evolutionary dynamics is modified, eventually allowing the population to escape faster from local optima and reach new zones of the fitness landscape. Given a pseudo-Boolean or continuous optimization static problem, the proposed framework can be used to dynamically change the problem to speed up the optimization. [ABSTRACT FROM AUTHOR]

Constraint propagation methods demonstrate splendid performance in constrained clustering tasks. Although some multi-modal constraint propagation methods have been proposed in recent years, a feasible and robust approach to multi-modal feature fusion in pairwise constraint propagation is still in demand. This paper presents a novel multi-modal fusion approach in order to cope with the constraint propagation on multi-modal datasets, called Multi-modal Fusion Learning (MFL). The proposed method can reach a multi-modal fusion based on the observed constraint information and the propagation process. It is capable of handling any number of modalities without any prior knowledge of each modality. We merge the fusion learning and constraint propagation into one unified problem and solve it by a bound-constrained quadratic optimization. Our proposed method has been tested in clustering tasks on two publicly available multi-modal datasets to show its superior performance. [ABSTRACT FROM AUTHOR]

Advances in Pareto optimization techniques have encouraged the study of their application to solve single-objective optimization problems. The motive is that the Pareto concept can be an effective approach to reduce the impacts of local optima. The most challenging task in developing such an approach is the reformulation of the target single-objective to multiple objectives. This paper proposes a new multi-objectivization approach by introducing an additional helper objective that is to be optimized with the primary objective simultaneously using Pareto local search. As a key feature, the additional objective is formulated as a function of the primary objective and penalties associated to solution features. The penalties are dynamically updated during the search, with the hope to guide the search to avoid non-promising features for the primary objective. Computational results on the traveling salesman problem and the quadratic assignment problem confirm the effectiveness of the proposed approach in comparison to other multi-objectivization approaches and state-of-the-art methods on these benchmarks. [ABSTRACT FROM AUTHOR]

Over the last two decades, many different differential evolution algorithms for solving optimization problems have been introduced. Although most of these algorithms used a single mutation strategy, several with multiple mutation strategies have recently been proposed. Multiple-operator-based algorithms have been proven to be more effective and efficient than single-operator-based algorithms for solving a wide range of benchmark and practical problems. In these algorithms, adaptive operator selection mechanisms are generally applied to place greater emphasis on the best-performing evolutionary operators based on their performance histories for generating new offspring. In this paper, we investigate using problem landscape information in an adaptive operator selection mechanism. For this purpose, a new algorithm, which considers both this problem landscape information and the performance histories of the operators, for dynamically selecting the most suitable differential evolution operator during the evolutionary process, is proposed. The contributions of each component of the selection mechanism are analyzed and the performance of the proposed algorithm is evaluated by solving 45 unconstrained optimization problems. The results demonstrate the effectiveness and superiority of the proposed algorithm to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Inspired by the intelligent foraging behavior of honey bees, the artificial bee colony algorithm (ABC), a swarm-based stochastic optimization method, has shown to be very effective and efficient for solving optimization problems. However, since its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. To better balance the tradeoff between exploration and exploitation, in this paper, we propose a depth-first search (DFS) framework. The key feature of the DFS framework is to allocate more computing resources to the food sources with better quality and easier to be improved for evolution. We apply the DFS framework to ABC, GABC and CABC, yielding DFSABC, DFSGABC and DFSCABC respectively. The experimental results on 22 benchmark functions show that the DFS framework can speed up convergence rate in most cases. To further improve the performance, we introduce two novel solution search equations: the first equation incorporates the information of elite solutions and can be applied to the employed bee phase, while the second equation not only exploits the information of the elite solutions but also employs the current best solution in the onlooker bee phase. Finally, two novel proposed search equations are combined with DFSABC to form a new variant of ABC, named DFSABC_elite. Through the comparison of DFSABC_elite with other variants of ABC and some non-ABC methods, the experimental results demonstrate that DFSABC_elite is significantly better than the compared algorithms on most of the test functions in terms of solution quality, robustness, and convergence speed. [ABSTRACT FROM AUTHOR]

Free-form surface part inspection can be conducted by comparing an ideal design model with a real measurement model. Because these two models are in different coordinate systems, the measurement model, represented by a set of 3D points, should be consistently registered to the design model. The final aim of the registration is to determine the optimal transformation matrix. In this research area, the iterative closest point (ICP) method is the best-known algorithm for the registration of two point sets. However, the ICP method needs a good initial parameter to obtain the global optimum transformation matrix, which is difficult to guarantee in the actual inspection process. To improve the precision and robustness of complex parts quality inspection, an ensemble parameters fruit fly optimization (EFFO) algorithm is proposed in this study. This paper provides a parameter pool using the smell-based search process of a fruit fly swarm and sorts the individuals based on the fitness to identify the leaders in a vision-based search process. Additionally, an ICP-based initialization strategy is introduced into the EFFO. We compared the EFFO algorithm with other registration methods and some variants of FFO. The proposed algorithm is superior to other algorithms in terms of accuracy and robustness, and the experimental results show that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

The power to solve intractable optimisation problems is often found through population based evolutionary methods. These include, but are not limited to, genetic algorithms, particle swarm optimisation, differential evolution and ant colony optimisation. While showing much promise as an effective optimiser, extremal optimisation uses only a single solution in its canonical form – and there are no standard population mechanics. In this paper, two population models for extremal optimisation are proposed and applied to a multi-objective version of the generalised assignment problem. These models use novel intervention/interaction strategies as well as collective memory in order to allow individual population members to work together. Additionally, a general non-dominated local search algorithm is developed and tested. Overall, the results show that improved attainment surfaces can be produced using population based interactions over not using them. The new EO approach is also shown to be highly competitive with an implementation of NSGA-II. [ABSTRACT FROM AUTHOR]

According to the literature, a linear optimization problem subjected to a system of bipolar fuzzy relational equations with max-Łukasiewicz composition can be translated into a 0–1 integer linear programming problem and solved using integer optimization techniques. However, the technique of integer optimization may involve hight computation complexity. To improve computational efficiency for solving such an optimization problem, this paper proves that each component of an optimal solution obtained from such an optimization problem can either be the corresponding component’s lower bound or upper bound value. Because of this characteristic, a simple value matrix with some simplified rules can be proposed to reduce the problem size first. A simple solution procedure is then presented for determining optimal solutions without translating such an optimization problem into a 0–1 integer linear programming problem. Two examples are provided to illustrate the simplicity and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study three selection mechanisms based on the maximin fitness function and we propose another one. These selection mechanisms give rise to the following MOEAs: “MC-MOEA”, “MD-MOEA”, “MH-MOEA” and “MAH-MOEA”. We validated them using standard test functions taken from the specialized literature, having from three up to ten objective functions. We compare these four MOEAs among them and also with respect to MOEA/D (which is based on decomposition), and to SMS-EMOA (which is based on the hypervolume indicator). Our preliminary results indicate that “MD-MOEA” and “MAH-MOEA” are promising alternatives for solving MOPs with either low or high dimensionality. [ABSTRACT FROM AUTHOR]

This paper presents novel second-order cone programming (SOCP) formulations that determine a linear multi-class predictor using support vector machines (SVMs). We first extend the ideas of OvO (One-versus-One) and OvA (One-versus-All) SVM formulations to SOCP-SVM, providing two interesting alternatives to the standard SVM formulations. Additionally, we propose a novel approach (MC-SOCP) that simultaneously constructs all required hyperplanes for multi-class classification, based on the multi-class SVM formulation (MC-SVM). The use of conic constraints for each pair of training patterns in a single optimization problem provides an adequate framework for a balanced and effective prediction. [ABSTRACT FROM AUTHOR]

Consistency of preference relations is related to rationality, the lack of consistency in decision making often leads to misleading solutions. This paper studies the ordinal and cardinal consistency problems of fuzzy reciprocal preference relations. First, the concept of ordinal consistency of fuzzy reciprocal preference relations is introduced and analyzed. A l p distance-based method is proposed to formulate the underlying optimization problems as goal programming (GP) models for ordinal and additive consistency problems respectively. By setting different p , three GP models are obtained: (a) a linear GP model where p = 1; (b) a MINMAX GP model, where p = ∞; and (c) an extended GP model, which integrates the two previous models in particular cases. Utilizing these models, we can solve the ordinal and additive consistency problems for fuzzy reciprocal preference relations. The proposed model can preserve the initial preference information as much as possible. Finally, a numerical example and comparative analysis are provided to show effectiveness and validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a basic variable neighborhood search for solving Minimum differential dispersion problem using only the swap neighborhood structure in both descent (intensification) and shaking (diversification) steps. It has become a trend in the metaheuristic literature to use hybrid metaheuristics, i.e., combination of several metaheuristic paradigms, for solving some particular optimization problem. We show that our simple method, which relies on the basic Variable neighborhood search, significantly outperforms the hybrid one that combines GRASP, Variable neighborhood search, and Exterior path relinking metaheuristics. Thus, simplicity is not only the desired user friendly property of a heuristic but can lead to more efficient and effective method than if complex hybrid metaheuristic is used: less is more. [ABSTRACT FROM AUTHOR]

Differential Evolution is an efficient metaheuristic for continuous optimization that suffers from the curse of dimensionality. A large amount of experimentation has allowed researchers to find several potential weaknesses in Differential Evolution. Some of these weaknesses do not significantly affect its performance when dealing with low-dimensional problems, so the research community has not paid much attention to them. The aim of this paper is to provide a better insight into the reasons of the curse of dimensionality and to propose techniques to alleviate this problem. Two different weaknesses are revisited and schemes for dealing with them are devised. The schemes increase the diversity of trial vectors and improve on the exploration capabilities of Differential Evolution. Some important mathematical properties induced by our proposals are studied and compared against those of related schemes. Experimentation with a set of problems with up to 1000 dimensions and with several variants of Differential Evolution shows that the weaknesses analyzed significantly affect the performance of Differential Evolution when used on high-dimensional optimization problems. The gains of the proposals appear when highly exploitative schemes are used. Our proposals allow for high-quality solutions with small populations, meaning that the most significant advantages emerge when dealing with large-scale optimization problems, where the benefits of using small populations have previously been shown. [ABSTRACT FROM AUTHOR]

Depth map is the basic requirement for all three-dimensional (3D) applications, but facing sensor noises, low frame-rate and low resolution in the procedure of data acquisition, especially in multiview cases. These problems bring obstacles to high quality 3D applications. Among the existing approaches, depth propagation is one of the promise approaches, and it can be utilized in temporal or spatial manner. However, propagation based algorithms process one aspect of the mentioned problems to pursuit local optimal solution. Actually, the process chain of depth map is from capture to application, and the optimization should be coupled instead of mutually independent. In this paper, we proposed a bundled-optimization scheme to process the thorough chain from capture to multiview dense depth map generation for the 3D applications. In this scheme, sensor noises in the captured low-resolution depth map are first detected and removed through a frequency-counting based non-linear filter. The filter refrains from the noise amplification in the procedure of depth map up-sampling. Low-pass blurring effect around high frequency areas is the by-product in up-sampling, and it is hard to detect in the depth map. We therefore propose a Blocklet based depth map optimization method for this blurring effect, and the accuracy of the high resolution depth map is then improved. Temporal depth propagation is then utilized on the optimized depth maps through the optical flow field rectified by temporal and spatial constrains. After that, a multi-set graph cut model is proposed to synthesize the multiview dense depth map. The experimental results indicate that our scheme can achieve at least 13.2575% PSNR gains when comparing to the benchmark depth map synthesis methods, and suggest the effectiveness of the proposed bundled-optimization method. [ABSTRACT FROM AUTHOR]

The problem of conditional distributivity for different two aggregation operators plays an important role in many different fields such as utility theory and integration theory. This paper deals with the conditional distributivity of a continuous nullnorm with respect to a uninorm with the continuous underlying t-norm and t-conorm. [ABSTRACT FROM AUTHOR]

Cooperative Coevolutionary algorithms (CC) have been successful in solving large scale optimization problems. The performance of CC can be improved by decreasing the number of interdependent variables among decomposed subproblems. This is achieved by first identifying dependent variables, and by then grouping them in common subproblems. This approach has potential because so far no grouping technique has been mainly developed for constrained problems. In this paper, a new variable interaction identification technique to identify the dependent variables in large scale constrained problems is proposed. The proposed technique is tested on both a new test suite of constrained problems with medium and high dimensions, which include overlapping subproblems and different levels of complexity and nonseparability and also the established DED problem. The experimental results have shown that the proposed technique contributes to the decomposition approach over a range of high dimensions, in comparison with other state-of-the art grouping techniques. It achieves better performance with higher feasibility ratios and less computational time. [ABSTRACT FROM AUTHOR]

Recent years have witnessed unprecedented volumes of structured data published in RDF format. Taking full advantage of such data has attracted a growing amount of research interest from both academia and industry. However, efficient processing of top- k queries in RDF data is still a new topic. Most existing approaches ignore top- k queries, or only provide a limited number of ranking functions. In this paper, we provide an effective and efficient processing algorithm for top- k queries that consists of a novel tree-style index MS-tree and MS-tree-based filtering and pattern-matching functions. Firstly, candidate entities in RDF data were efficiently ranked and filtered through an MS-tree-based top-down method. Then, query structure patterns are matched in RDF data through graphic exploration. In order to handle more complex scoring functions, a dynamic variable selecting optimization is employed to accelerate the threshold decrease. We evaluate our solutions with both synthetic and real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A large number of contemporary research efforts focus on incomplete data streams handling. These efforts, usually, focus on the creation and maintenance of top- k lists useful to provide efficient responses to top- k queries. In case of large volumes of data accumulated at high rates the problem becomes more intense as an efficient method for maintaining the top- k list is considered imperative. In this paper, we focus on the behavior of an Observer Entity (OE) responsible to observe the incoming data and initiate the maintenance process of the top-k list . The maintenance process involves the calculation of a score for each object and the update of the top- k list. We adopt the principles of Optimal Stopping Theory (OST) and introduce a scheme that determines, in the appropriate time, when the OE decides to initiate the maintenance process. Due to the incomplete data setting, the high rate of the incoming data and the large volume of data, the maintenance process is initiated only when the OE has the appropriate amount of information for providing a top- k list. In contrast to other research approaches, we do not maintain any additional sets of objects. Instead, we attempt to minimize the number of the necessary processing tasks over the list of objects. We present a mathematical analysis for our scheme and an extensive experimental evaluation. The comparison with other models shows that the proposed model provides efficiency in the list management and minimizes the required time to result the final top- k list. [ABSTRACT FROM AUTHOR]

In some real-world decision processes, decision makers may prefer to provide their opinions using linguistic expressions instead of a single linguistic term. Particularly, they may hesitate between several linguistic terms. In this paper, we deal with the consensus issue in the hesitant linguistic group decision making (GDM) problem. Firstly, a novel distance-based consensus measure is proposed. Then, using this consensus measure we develop an optimization-based consensus model in the hesitant linguistic GDM, which minimizes the number of adjusted simple terms in the consensus building. Furthermore, a two-stage model is displayed to further optimize the solutions to the proposed consensus model, through which we obtain the unique optimal adjustment suggestion to support the consensus reaching process in the hesitant linguistic GDM. Finally, several desirable properties are proposed to justify the proposal, and two examples are used to demonstrate the validity of the models. [ABSTRACT FROM AUTHOR]

Bi-objective optimization of a multi-product multi-period three-echelon supply-chain-network problem is aimed in this paper. The network consists of manufacturing plants, distribution centers (DCs), and customer nodes. To bring the problem closer to reality, the majority of the parameters in this network including fixed and variable costs, customer demand, available production time, set-up and production times, all are considered stochastic. The goal is to determine the quantities of the products produced by the manufacturing plants in different periods, the number and locations of the warehouses, the quantities of products transported between the supply chain entities, the inventory of products in warehouses and plants, and the shortage of products in periods such that both the expected and the variance of the total cost are minimized. The problem is first formulated into the framework of a single-objective stochastic mixed integer linear programming model. Then, it is reformulated into a bi-objective deterministic mixed-integer nonlinear programming model. To solve the complicated problem, a non-dominated sorting genetic algorithm (NSGA-II) is utilized next. As there is no benchmark available in the literature, another GA-based algorithm called non-dominated ranking genetic algorithm (NRGA) is used to validate the results obtained. In both algorithms, a modified priority-based encoding is proposed. Some numerical illustrations are provided at the end to not only show the applicability of the proposed methodology, but also to select the best method using a t -test along with the simple additive weighting (SAW) method. [ABSTRACT FROM AUTHOR]

In case-based reasoning (CBR), the weights of feature attributes directly affect the quality of problem solving. This paper proposes a membrane computing (MC)-based approach to optimize the attribute weights. A cell-like membrane structure with three layers is designed. The initial weight objects are then obtained by a global search using selection, crossover, mutation, and a two-way communication rule. Subsequently, the best weight object is obtained using a simulated annealing (SA) algorithm between the membranes. In this manner, a group of weight objects is received for CBR problem solving. The experiment results show that the classification accuracy of this method is higher compared with the entropy method, genetic algorithms, SA, and the neural networks method. The application of MC can obtain more reasonable attribute weights, which can effectively improve the quality of problem solving for a CBR system. [ABSTRACT FROM AUTHOR]

Highlights: [•] The paper studies a hybrid flowshop scheduling problem with total flowtime criterion. [•] The algorithm employed is a novel migrating birds optimization (MBO). [•] Some advanced and effective techniques are introduced to improve the MBO. [•] Computational and statistical analyses show the superiority of the presented approach. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a new preference relation based on a reference point approach. This relation offers an easy approach to integrate decision maker’s preferences into a MOEA without modifying its basic structure. Besides finding the optimal solution of the achievement scalarizing function, the new preference relation allows the decision maker to find a set of solutions around that optimal solution. Then, a MOEA equipped with the proposed preference relation can be integrated into an interactive optimization method. One of the main advantages of the new method is that setting its parameters is an intuitive task to the decision maker. The other advantage is that, since our preference relation induces a finer order on vectors of objective space than that achieved by the Pareto dominance relation, it is appropriate to cope with problems having a high number of objectives. We evaluated the proposed preference relation an engineering problem, the optimization of an airfoil design with 6 objectives. [Copyright &y& Elsevier]

Abstract: Nature-inspired algorithms for optimization are significant topics in the areas of computational intelligence. The contribution of this paper is to present a new heuristic intelligent evolutionary algorithm based on membrane systems to solve the global numerical optimization problems. The proposed algorithm employs the fundamental ingredients of membrane systems, including multisets, reaction rules and membrane structure. In addition, the proposed algorithm incorporates information of the adjacent symbol-objects, to guide the evolution toward the global optimum, efficiently. More specifically, symbol-objects are evolved by the cellular automata model which invokes the rewrite rules to exchange the information of the adjacent symbol-objects. Moreover, sharing information in the skin membrane is implemented, which accelerates the speed of the proposed algorithm to find the global optimal solution. In the extensive experimental study, the effectiveness of the proposed algorithm is demonstrated with the benchmark global numeric optimization problems. The experimental results indicate that the proposed method is a competitive optimizer in comparison with the four state-of-the-art evolutionary algorithms. [Copyright &y& Elsevier]

Abstract: Swarm-based algorithms have acquired an important role in solving real-world optimization problems. In this paper, Kinetic Gas Molecule Optimization (KGMO), an optimization algorithm that is based on the kinetic energy of gas molecules, is introduced. The agents are gas molecules that are moving in the search space; they are subject to the kinetic theory of gases, which defines the rules for gas molecule interactions in the model. The performance of the proposed algorithm, in terms of its ability to find the global minima of 23 nonlinear benchmark functions, is evaluated against the corresponding results of two well-known benchmark algorithms, namely, Particle Swarm Optimization (PSO) and the recently developed high-performance Gravitational Search Algorithm (GSA). The simulations that were undertaken indicate that KGMO achieves better results in decreasing the Mean Square Error (MSE). Significant improvements of up to 107 and 1020 times were achieved by KGMO against PSO and GSA, respectively, in solving unimodal benchmark functions within 150 iterations. Improvements of at least tenfold were achieved in solving the multimodal benchmark functions. The proposed algorithm is more accurate and converges faster than does the benchmark algorithms, which makes this algorithm especially useful in solving complex optimization problems. [Copyright &y& Elsevier]

Abstract: Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches. [Copyright &y& Elsevier]