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Adversarial sample generation problem is a hot issue in the security field of deep learning. Evolutionary algorithm has been widely used to solve this problem in recent years because of its good global search ability. However, existing methods still suffer from the "curse of dimensionality" when attacking high-resolution images. In this paper, a two-stage frequency domain generation algorithm of black-box adversarial samples based on differential evolution is proposed. In the first stage, a representative image-guided differential evolution method is proposed to quickly generate a universal adversarial perturbation with a high attack success rate in the frequency-domain. In the second stage, a space reduction strategy based on frequency-domain pixel blocks is designed to reduce the search space and alleviate the problem of "curse of dimensionality". In addition, a new space–frequency interaction sensitivity measure is introduced to evaluate the similarity between the adversarial samples and the original images. The adversarial perturbations obtained by the measure are more in line with the subjective perception of the human eye. Finally, compared with several typical black-box adversarial sample generation algorithms, experimental results show that the proposed algorithm can achieve higher attack success rate with less prediction times. • Proposing a two-stage evolutionary generation framework of adversarial samples. • Developing a space–frequency interaction sensitivity measure. • Presenting a representative image-guided differential evolutionary algorithm. • Designing an individual adversarial perturbation generation algorithm. [ABSTRACT FROM AUTHOR]

The Multi-Objective Discrete Network Design Problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin–destination pair instead of shared arterials. [ABSTRACT FROM AUTHOR]

This paper characterizes a class of repeated moral hazard problem where the agent's replacement action is necessary for assuring the performance of important materials and equipment. However, due to the principal's costly monitoring, the continuously decreasing performance caused by the agent's successive shirking behaviors will significantly harm the principal's profits or welfare. For sake of performance assurance and monitoring efficiency, this paper adopts two reinforcement learning algorithms to find and test the strategies on the final path satisfying the conditions of (Restricted) Experience Based Equilibrium. Experimental results in a case of quality assurance of grain demonstrate the effectiveness of the model and the solution methods. Practically, this framework especially fits a kind of government-enterprise relation, such as in the field of emergency materials management and safety production management. Given the increasing need of developing systems for the government's management, this paper aims to provide applicable strategies and guidelines from a theoretical perspective. [ABSTRACT FROM AUTHOR]

In complex networks, there are a huge number of overlapping community structures, and those structures have been an increasing concern in recent years. The label propagation algorithm (LPA) family is a kind of mainstream method that discovers different community structures in complex networks. But these methods often struggle with stability and feasibility, as well as issues sensitive to network size. In this paper, to solve the above problems and improve the detection accuracy, we present a fast label propagation algorithm (FLPA) based on node influence and label weight. In FLPA, we first use the graph compression technique to reduce the network size. Then, we propose a new node influence calculation method and fuse it with the α -path similarity to precisely control the label weight in the label propagation stage. Through the above process, FLPA makes the labels that are propagated and received by nodes more reasonable and further improves the detection accuracy. Finally, we restore the network and make the compressed node belong to the same community as its corresponding super node. Experimental results on 10 real and 58 synthetic networks indicate that FLPA is suitable for detecting overlapping community structures regardless of network scale and accomplishes better than state-of-the-art methods on stability and feasibility. [ABSTRACT FROM AUTHOR]

Multi-agent reinforcement learning (MARL) has been successfully applied in many fields. In MARL, the policy evaluation problem is one of crucial problems. In order to solve this problem, distributed Temporal-Difference (TD) learning algorithm is one of the most popular methods in a cooperative manner. Despite its empirical success, however, the theory of the adaptive variant of distributed TD learning still remain limited. To fill this gap, we propose an adaptive distributed temporal-difference algorithm (referred to as MS - ADTD) under Markovian sampling over time-varying networks. Furthermore, we rigorously analyze the convergence of MS - ADTD , the theoretical results show that the local estimation can converge linearly to the optimal neighborhood. Meanwhile, the theoretical results are verified by simulation experiments. • This paper proposes a distributed adaptive TD learning algorithm under Markovian sampling. • It theoretically analyzes the non-asymptotic convergence performance of the proposed algorithm. • It verifies the performance of the proposed algorithm by experiments. [ABSTRACT FROM AUTHOR]

A large number of metaheuristic algorithms have been proposed in the last three decades, but no metaheuristic algorithm is superior to the others for all the optimization problems. It is laborious to select an appropriate metaheuristic algorithm to solve a problem, especially for laymen. In this paper, a framework for co-evolutionary algorithm using Q-learning with the meme is proposed, which is called as QLMA. The solution generation method in the metaheuristic algorithm is named meme, which is also viewed as the action for an agent in Q-learning, and multiple memes form the action set. In the initial stage, the tent map and opposition based learning are employed to obtain the initial population. In updating stage, a new population is generated by an action that is chosen from the action set by the agent in Q-learning, then the disruption operation is applied, avoiding excessive aggregation of solutions around the current global optimal solution and improving the balance between exploration and exploitation. QLMA is compared to sixteen algorithms on 23 classical benchmark functions, CEC 2017, and CEC 2019 benchmark functions. The experimental results demonstrate that QLMA is superior to the peer algorithms and has a good balance between exploration and exploitation. [ABSTRACT FROM AUTHOR]

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k -labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection by achieving better coverage of labels and inter-label correlations. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared to the RAKEL algorithm and to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Highlights: [•] Aiming at the problem that how to classify T-splines this paper gives a mathematical analysis, and a sufficient condition of standard T-splines is also given. [•] Then this paper presents an algorithm of determining the T-spline classification, and we can get the inherently mathematical properties of T-splines by the algorithm. [ABSTRACT FROM AUTHOR]

Resource transfers and uncertain activity durations are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized multi-project scheduling problem with both resource transfers and uncertain activity durations, we name it the stochastic decentralized multi-project scheduling problem with resource transfers (SDRCMPSPTT). To tackle the curse-of-dimensionality of an exact solution approach, we develop a new and effective rollout policy-based approximate dynamic programming (ADP) algorithm for SDRCMPSPTT. Based on the benchmark instances, we build a new data set and examine the performance of 12 priority rule heuristics, then select the two best ones as the base policy for our rollout algorithm. The proposed approach is verified under a stochastic environment by assessing different base heuristic policies, computational results show that the rollout algorithm can further improve the solution quality of the base heuristics. Compared with the state-of-the-art algorithm for solving the decentralized multi-project scheduling problem under a deterministic environment, our rollout policy-based ADP algorithm can also obtain competitive solutions. • An MDP model is established to formulate the problem studied in this paper. • A new rollout-based ADP algorithm is developed to obtain closed-loop solutions. • Rollout algorithm performs better than the base heuristics significantly. • The rollout algorithm is further verified by solving deterministic problems. [ABSTRACT FROM AUTHOR]

• Automatic Artificial Intelligence Algorithm Selection. • Automatic Hyperparameter Optimization. • Particle Swarm Optimization which can handle continuous and discrete values. • Particle Swarm Optimization with Simulated Annealing acceptance criteria. • Global Optimal Solution. Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way. [ABSTRACT FROM AUTHOR]

This study considers a constrained huge-scale optimization problem over networks where the objective is to minimize the sum of nonsmooth local loss functions. To solve this problem, many optimization algorithms have been proposed by employing (sub)gradient descent methods to handle high-dimensional data, but the computation of the entire sub(gradient) becomes a computational bottleneck. To reduce the computational burden of each agent and preserve privacy of data in time-varying networks, we propose a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm over time-varying networks, in which the coordinates of the subgradient vector is randomly chosen to update the optimized parameter and the partially homomorphic cryptography is used to protect the privacy of data. Further, we prove that our algorithm is convergent asymptotically. Moreover, the rates of convergence are also established by choosing appropriate step sizes, i.e., O (log K / K) under local strong convexity and O (log K / K) under local convexity, in which K represents the number of iterations. Meanwhile, we show that the privacy of data can be protected by the proposed algorithm. The results of experiments demonstrate the computational benefit of our algorithm on two real-world datasets. The theoretical results are also verified by different experiments. • This paper proposes a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm. • It proves that the proposed algorithm is asymptotically convergent. • It proves that the proposed algorithm can protect the privacy of data. • It proves that the rate of convergence is achieved, i.e., O (log K / K) and O (log K / K). [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate the optimal locations of surveillance cameras on straight lanes. As known, inappropriate settings of surveillance cameras will result in some dead angles and dead space, and oversetting of surveillance cameras will waste limited resources. The considered location problem aims to minimize the maximal detection failure probability of points on straight lane subject to a limited budget. The problem is an important and complex location problem in practice. The purpose of this paper is twofold. Firstly, exact closed forms of optimal locations of single-type surveillance cameras are derived. Secondly, we apply a PSO algorithm for solving the types and locations of multiple-type surveillance cameras. Numerical results are reported and compared with those by well known software LINGO. Limited numerical results show the effectiveness of PSO especially when the budget is limited. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new fault prediction model is presented to deal with the fault prediction problems in the presence of both quantitative and qualitative data based on the dynamic evidential reasoning (DER) approach. In engineering practice, system performance is constantly changed with time. As such, there is a need to develop a supporting mechanism that can be used to conduct dynamic fusion with time, and establish a prediction model to trace and predict system performance. In this paper, a DER approach is first developed to realize dynamic fusion. The new approach takes account of time effect by introducing belief decaying factor, which reflects the nature that evidence credibility is decreasing over time. Theoretically, it is show that the new DER aggregation schemes also satisfy the synthesis theorems. Then a fault prediction model based on the DER approach is established and several optimization models are developed for locally training the DER prediction model. The main feature of these optimization models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune the DER prediction model whose initial parameters are decided by expert’s knowledge or common sense. Finally, two numerical examples are provided to illustrate the detailed implementation procedures of the proposed approach and demonstrate its potential applications in fault prediction. [ABSTRACT FROM AUTHOR]

Abstract: Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study. [Copyright &y& Elsevier]

The time overhead is huge and the clustering quality is unstable when running the K-means algorithm on massive raw data. To solve these problems, the concept of the influence space is introduced, and on this basis, a new clustering algorithm named ISBFK-means based on the influence space is proposed in this paper. First, the influence space divides the given data set into multiple small regions. Then, the representative data objects in each region are obtained to form a new data set, in which the class labels of representative data objects are those of all the data objects in the correlation influence space. Next, the K-means clustering is performed on the new data set, thereby obtaining the final clustering result. Theoretical analysis and experimental results show that this approach effectively reduces the amount of data in the clustering process and improves the stability of clustering quality. As a major feature of this work, the celestial spectral data observed by the LAMOST survey are especially employed to verify the algorithm ISBFK-means. The experimental results indicate that this algorithm has higher performance than other similar algorithms on the correctness, efficiency and sensitivity to the quality of spectral data. • The impact of outliers on clustering results is reduced using the influence space. • Representative data objects of the original datasets are extracted under the influence space. • A clustering algorithm called ISBFK-means is proposed based on influence space. • Valuable information hidden in LAMOST low-quality spectra is revealed by ISBFK-means algorithm. [ABSTRACT FROM AUTHOR]

Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithm for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above. We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch. [ABSTRACT FROM AUTHOR]

Generally, training images are essential for a computer vision model to classify specific object class accurately. Unfortunately, there exist countless number of different object classes in real world, and it is almost impossible for a computer vision model to obtain a complete training images for each of the different object class. To overcome this problem, zero-shot learning algorithm was emerged to learn unknown object classes from a set of known object classes information. Among these methods, attributes and image hierarchy are the widely used methods. In this paper, we combine both the strength of attributes and image hierarchy by proposing Attributes Relationship Model (ARM) to perform zero-shot learning. We tested the efficiency of the proposed algorithm on Animals with Attributes (AwA) dataset and manage to achieve state-of-the-art accuracy (50.61%) compare to other recent methods. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is recognized as one of the majority public and challenging problems in the Machine Learning domain. FS can be examined as an optimization problem that needs an effective optimizer to determine its optimal subset of more informative features. This paper proposes a wrapper FS method that combines chaotic maps (CMs) and binary Group Search Optimizer (GSO) called CGSO, which is used to solve the FS problem. In this method, five chaotic maps are incorporated with the GSO algorithm's main procedures, namely, Logistic, Piecewise, Singer, Sinusoidal, and Tent. The GSO algorithm is used as a search strategy, while k-NN is employed as an induction algorithm. The objective function is to integrate three main objectives: maximizing the classification accuracy value, minimizing the number of selected features, and minimizing the complexity of generated k-NN models. To evaluate the proposed methods' performance, twenty well-known UCI datasets are used and compared with other well-known published methods in the literature. The obtained results reveal the superiority of the proposed methods in outperforming other well-known methods, especially when using binary GSO with Tent CM. Finally, it is a beneficial method to be utilized in systems that require FS pre-processing. • A novel feature selection method is proposed for classification task. • Five Chaotic Group Search Optimizers are investigated. • Twenty UCI datasets were used in the experiments. • Our methods got a higher accuracy results using a fewer number of features. [ABSTRACT FROM AUTHOR]

The component pick-and-place sequence is one of the key factors to affect the working efficiency of the surface mounting machine in the printed circuit board assembly. In this paper, an improved Shuffled Frog-leaping Algorithm was presented by improving the basic Shuffled Frog-leaping Algorithm (SFLA) with the strategy of letting all frogs taking part in memetic evolution and adding the self-variation behavior to the frog. The objective function of component pick-and-place sequence of the gantry multi-head component surface mounting machine was established. Parameters selection is critical for SFLA. In this study, Three-way ANOVA was used in parameters analyzing of the new improved SFLA. The parameters like memeplex numbers m, the frogs' number P and local evolution numbers iPart were found having notable effects on the mounting time (time spent for components picking and placing), but the interactions among these parameters were not obvious. Multiple comparison procedures were adopted to determine the best parameter settings. In order to test the performance of the new algorithm, several experiments were carried out to compare the performance of improved SFLA with the basic SFLA and the genetic algorithm (GA) in solving the component pick-and-place sequence optimization problems. The experiment results indicate that improved SFLA can solve the optimization problem efficiently and outperforms SFLA and GA in terms of convergence accuracy, although more CPU time is undeniably needed. [ABSTRACT FROM AUTHOR]

Wind energy has become the world's fastest growing energy source. Although wind farm layout is a well known problem, its solution used to be heuristic, mainly based on the designer experience. A key in search trend is to increase power production capacity over time. Furthermore the production of wind energy often involves uncertainties due to the stochastic nature of wind speeds. The addressed problem contains a novel aspect with respect of other wind turbine selection problems in the context of wind farm design. The problem requires selecting two different wind turbine models (from a list of 26 items available) to minimize the standard deviation of the energy produced throughout the day while maximizing the total energy produced by the wind farm. The novelty of this new approach is based on the fact that wind farms are usually built using a single model of wind turbine. This paper describes the usage of multi-objective evolutionary algorithms (MOEAs) in the context of power energy production, selecting a combination of two different models of wind turbine along with wind speeds distributed over different time spans of the day. Several MOEAs variants belonging to the most renowned and widely used algorithms such as SPEA2 NSGAII, PESA and msPEA have been investigated, tested and compared based on the data gathered from Cancun (Mexico) throughout the year of 2008. We have demonstrated the powerful of MOEAs applied to wind turbine selection problem (WTS) and estimate the mean power and the associated standard deviation considering the wind speed and the dynamics of the power curve of the turbines. Among them, the performance of PESA algorithm looks a little bit superior than the other three algorithms. In conclusion, the use of MOEAs is technically feasible and opens new perspectives for assisting utility companies in developing wind farms. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we address the exact solution of the vertex bisection problem (VBP). We propose two novel B&B algorithms to solve VBP, which include new upper and lower bound constructive heuristics, and an efficient strategy to explore the combinatorial search space. The computational results show that the proposed algorithms clearly outperforms the state-of-the-art B&B algorithm in quality and efficiency. The two proposed B&B versions differ in the exploration strategy and the storage of the search tree. Also, we provide four new solutions, previously unknown. We consider that the main contributions of this work can be adapted to solve combinatorial problems in other related domains. • Two representations of the search tree for the branch and bound algorithm. • Two new greedy constructive heuristics for the vertex bisection problem. • A new lower bound computation for the vertex bisection problem. • New optimal solutions for four Harwell–Boing instances. [ABSTRACT FROM AUTHOR]

• Investigated a large-scale heterogeneous container loading problem. • Considered weight limits and suspension constraints in this problem. • Integrated ALNS and heuristic packing algorithm for the presented problem. • Performed experiments on real-world instances. • Verified the superiority of HALNS by comparing with existing methods. This paper aims to solve the large-scale heterogeneous container loading problem (HCLP), which is an extensive form of the multiple container loading problem, in a limited time. The target is to choose a set of containers of different sizes to accommodate all products and minimize the wasted space rate. Although the heterogeneous container selection problem is a general problem in the logistics industry, few related studies have been conducted. This study also considers some practical constraints, such as weight limits and suspension constraints. A hybrid adaptive large neighborhood search (HALNS) algorithm, which includes a set of original destroy-repair operators, especially for heterogeneous container selection problems, and integrates a heuristic packing algorithm, is proposed to solve the problem in an acceptable time. To verify the efficiency of the proposed algorithm, computational experiments on real-world instances from a multinational logistics company are performed, and the results are compared with those of other existing algorithms. The results indicate that the proposed algorithm outperforms other algorithms for the HCLP. [ABSTRACT FROM AUTHOR]

Students generally attempt to improve themselves with different manners in their spare time. Inspired by this fact, this paper presents a multi-objective elitist feedback teaching–learning-based optimization (MEFTO) algorithm for multi-objective optimization problems. To promote the exploration capacity and convergence of the basic teaching–learning-based optimization (TLBO), a feedback phase that simulates the spare-time learning phenomenon is introduced at the end of the learner phase. Students should compare their score with the average class score to select an appropriate means for further improvement. Poorly performing students can learn from the teacher directly for rapid improvement, whereas high-performing students prefer to motivate themselves for reinforcement learning. Non-dominated sorting is incorporated to permit this heuristic to solve problems with several objectives. Crowding distance calculation is adopted to maintain the diversity of the obtained solution set in a single run. Elitism strategy is also employed to provide a great improvement for the algorithm. The performance of the MEFTO algorithm is compared with seven other well-known algorithms by using a set of unconstrained benchmark test problems and three constrained engineering optimization problems. The qualitative and quantitative results indicate that the proposed algorithm can provide considerably competitive results and outperforms the other algorithms. • A novel multi-objective evolutionary algorithm (MEFTO) is developed. • The developed algorithm is evaluated on a series of benchmark functions. • The performance of the MEFTO is demonstrated using various metrics. • The developed algorithm is applied to solve three constrained engineering problem. [ABSTRACT FROM AUTHOR]

Abstract: Some of the real-world problems are represented with just one label but many of today’s issues are currently being defined with multiple labels. This second group is important because multi-label classes provide a more global picture of the problem. From the study of the characteristics of the most influential systems in this area, MlKnn and RAkEL, we can observe that the main drawback of these specific systems is the time required. Therefore, the aim of the current paper is to develop a more efficient system in terms of computation without incurring accuracy loss. To meet this objective we propose MlCBR, a system for multi-label classification based on Case-Based Reasoning. The results obtained highlight the strong performance of our algorithm in comparison with previous benchmark methods in terms of accuracy rates and computational time reduction. [Copyright &y& Elsevier]

• A new matrix factorization strategy is designed for Nyström spectral clustering. • Incomplete Cholesky decomposition is introduced to accelerate Nyström approximation. • An efficient Nyström spectral clustering algorithm called ICD-NSC is proposed. • The effectiveness of ICD-NSC is demonstrated by comprehensive experiments. Nyström method can estimate the eigenvectors of a large kernel matrix with the eigenvectors of a small sampled sub-matrix. However, we may encounter two problems when using Nyström method to speed up spectral clustering: one problem is the approximate eigenvectors generated by standard Nyström method are sub-optimal, so they may impair the performance of spectral clustering; another one is the accurate Nyström approximation needs a sufficient number of samples, which will increase the eigen-decomposition cost on the sampled sub-matrix. To solve these problems, this paper proposes an efficient Nyström spectral clustering algorithm using incomplete Cholesky decomposition, in which a new matrix factorization strategy is designed for Nyström spectral clustering to meet the orthogonal constraints, and an efficient eigensolver based on incomplete Cholesky decomposition is developed to accelerate the Nyström approximation. In this way, the obtained approximate orthogonal eigenvectors will help to improve the clustering quality, and the developed eigenvector calculation method will help to reduce the clustering complexity. The experimental results show that the proposed algorithm performs well on many challenging data sets, and it can accomplish more complex clustering tasks with limited computing resources. [ABSTRACT FROM AUTHOR]

Tourism recommender system (TRS) experienced high growth over the last decades given its significant role in tourist satisfaction. Tourists are usually worried about planning their trips since it requires a considerable effort and it is a time consuming task. The main purpose of TRS is to select points of interest (POIs) that match users preferences and to suggest personalized daily sightseeing Designing such a suitable traveling tour is modeled as a Tourist Trip Design Problem (TTDP). Numerous variants of the problem are presented in the literature such as the Team Orienteering Problem (TOP) evoked in this paper. The aim is to maximize the total collected score of visited POIs subject to distance and time constraints. For solving the TTDP, we propose a metaheuristic based on two steps (i) grouping the POIs into clusters based on the start and end locations and (ii) solving the routing problem for each cluster to generate the optimal routes. The proposed approach is a combination between k-means and simulated annealing algorithms, namely, KSA. Besides, we develop StayPlan which is a TRS that integrates KSA algorithm to tackle the TTDP. StayPlan incorporates three main modules which are, user preferences, data retrieving & scoring and TTDP solving & optimization. Computational experiments compare the performance of our proposed algorithm with four state-of-the-art methods. The results show the efficiency and effectiveness of KSA algorithm in solving the benchmark suite from the literature. KSA algorithm reaches the best-known solutions for 100% of tested instances. Additionally, our method outperforms all the considered algorithms in terms of CPU time, it is quite fast with an average run time of 31.49 s. Ultimately, an illustrative example of the adapted scenario is applied to Tunis city as a real case. • Support tourists to have a suitable trip planning with optimal traversed routes. • Select points of interest based on users preferences and suggest suitable tours. • Solve the tourist trip design problem using a simulated-based metaheuristic. • A tourist recommender system is developed by integrating the proposed approach. • A comparative study with recent state-of-the-art algorithms is carried out. [ABSTRACT FROM AUTHOR]

In this paper, a new compact algorithm is proposed. Two sinusoidal formulas are used to automatically adjust the crossover rate and the mutation scaling factor in the compact Differential Evolution (cDE) metaheuristic. The proposed algorithm, called Compound Sinusoidal cDE, CScDE, is compared to seven state-of-the-art compact algorithms on the well-known BBOB test-bed, the CEC-2014 test suite for continuous optimisation, as well as five real-world optimisation problems chosen from the CEC-2011 benchmarks. The CScDE algorithm outperformed its competitors for most problem categories and over most dimensions. It is also compared to some well-established population-based metaheuristics. • A compact compound sinusoidal differential evolution, CScDE, is proposed. • CScDE uses sinusoidal formulas to adjust the parameters of compact DE. • Both binomial and exponential crossover are tested. • CScDE is applied on 89 reference problems with several dimensions. • CScDE outperformed state-of-the-art compact algorithms. [ABSTRACT FROM AUTHOR]

• A two-stage evolution strategy is proposed for solving multi-objective problem. • The convergence and diversity should be balanced in multi-objective optimization. • A local searching method can improve the diversity of solutions in the space. • Experimental results have been presented by using statistical method. The balance of convergence and diversity plays a significant role to the performance of multi-objective evolutionary algorithms (MOEAs). The MOEA/D is a very popular multi-objective optimization algorithm and has been used to solve various real world problems. Like many other algorithms, the MOEA/D also has insufficient ability of convergence and diversity when tackling certain complex multi-objective optimization problems (MOPs). In this paper, a novel algorithm named MOEA/D-TS is proposed for effectively solving MOPs. The new algorithm adopts two stages evolution strategies, the first stage is focused on pushing the solutions into the area of the Pareto front and speeding up its convergence ability, after that, the second stage conducts in the operating solution's diversity and makes the solutions distributed uniformly. The performance of MOEA/D-TS is validated in the ZDT, DTLZ and IMOP problems. Compared with others popular and variants algorithms, the experimental results demonstrate that the proposed algorithm has advantage over other algorithms with regard to the convergence and diversity in most of the tested problems. [ABSTRACT FROM AUTHOR]

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. • An efficient Multi-objective Evolutionary Swarm Hybrid algorithm is proposed. • Development of a nonlinear model to operational control of Hydro-power plants. • Hydro-power plant data regression obtains the maximum efficiency of the power units. • Efficiency energy goals achieved an increasing the profit in the energy production. [ABSTRACT FROM AUTHOR]

• We are the first to address the NP-hard Budgeted Maximum Coverage Problem. • We propose an algorithm that combines reinforcement learning with local search. • We introduce the first set of benchmark instances for the problem. • The proposed algorithm outperforms the comparison algorithms and the CPLEX solver. The Budgeted Maximum Coverage Problem (BMCP) is a general model with a number of real-world applications. Given n elements with nonnegative profits, m subsets of elements with nonnegative weights and a total budget, the BMCP aims to select some subsets such that the total weight of the selected subsets does not exceed the budget, while the total profit of the associated elements is maximized. BMCP is NP-hard and thus computationally challenging. We investigate for the first time an effective practical algorithm for solving this problem, which combines reinforcement learning and local search. The algorithm iterates through two distinct phases, namely a tabu search phase and a probability learning based perturbation phase. To assess the effectiveness of the proposed algorithm, we show computational results on a set of 30 benchmark instances introduced in this paper and present comparative studies with respect to the approximation algorithm, the genetic algorithm and the CPLEX solver. [ABSTRACT FROM AUTHOR]

• A novel variant of salp swarm algorithm is proposed. • The proposal fruitfully employs three simple but effective methodologies. • It is applied to 35 benchmark test functions and four real-life application problems. • Results are widely compared to the relevant results in literature. • The findings are highly impressive, ratifying the overt potential of this work. This paper propounds a modified version of the salp swarm algorithm (mSSA) for solving optimization problems more prolifically. This technique is refined from the base version with three simple but effective modifications. In the first one, the most important parameter in SSA responsible for balancing exploration and exploitation is chaotically changed by embedding a sinusoidal map in it to catch a better balance between exploration and exploitation from the first iteration until the last. As a short falling, SSA can't exchange information amongst leaders of the chain. Therefore, a mutualistic relationship between two leader salps is included in mSSA to raise its search performance. Additionally, a random technique is systematically applied to the follower salps to introduce diversity in the chain. This can be since there may be some salps in the chain that do not necessarily follow the leader for exploring unvisited areas of the search space. Several test problems are solved by the advocated approach and results are presented in comparison with the relevant results in the available literature. It is ascertained that mSSA, despite its simplicity, significantly outperforms not only the basic SSA but also numerous recent algorithms in terms of fruitful solution precision and convergent trend line. [ABSTRACT FROM AUTHOR]

• The Improved Slime Mould Algorithm (ISMA) has been derived and proposed. • The economic emission dispatch problem is solved using ISMA and other algorithms. • The proposed ISMA outperforms SMA, PSO, and TSA in solving the EED problem. • Both single- and multi-objective functions are solved using the proposed ISMA. In this paper, an Improved version of the Slime Mould Algorithm (ISMA) is proposed and applied to efficiently solve the single-and bi-objective Economic and Emission Dispatch (EED) problems considering valve point effect. ISMA is developed to improve the performance of the conventional Slime Mould Algorithm (SMA). In ISMA, the solution positions are updated depending on two equations borrowed from the sine–cosine algorithm (SCA) to obtain the best solution. Multi-objective SMA (MOSMA) and Multi-objective ISMA (MOISMA) are developed based on the Pareto dominance concept and fuzzy decision-making. In the multi-objective EED problem, MOSMA and MOISMA are applied to minimize the total fuel costs and total emission with the valve point effect simultaneously. The proposed single-and bi-objective economic emission dispatch algorithms are validated using five test systems, 6-units, 10-units, 11-units, 40-units, and 110-units. The performance of the proposed algorithm is compared with Harris Hawk Optimizer (HHO), Jellyfish Search optimizer (JS), Tunicate Swarm Algorithm (TSA), Particle swarm optimization (PSO), and SMA algorithms. The results show that the proposed algorithms are more robust than other well-known algorithms. Feasible solutions using the proposed algorithms are also achieved, which adjust the schedule of generation without violation of the operating generation limits. [ABSTRACT FROM AUTHOR]

Abstract: Many national and international governments establish organizations for applied science research funding. For this, several organizations have defined procedures for identifying relevant projects that based on prioritized technologies. Even for applied science research projects, which combine several technologies it is difficult to identify all corresponding technologies of all research-funding organizations. In this paper, we present an approach to support researchers and to support research-funding planners by classifying applied science research projects according to corresponding technologies of research-funding organizations. In contrast to related work, this problem is solved by considering results from literature concerning the application based technological relationships and by creating a new approach that is based on latent semantic indexing (LSI) as semantic text classification algorithm. Technologies that occur together in the process of creating an application are grouped in classes, semantic textual patterns are identified as representative for each class, and projects are assigned to one of these classes. This enables the assignment of each project to all technologies semantically grouped by use of LSI. This approach is evaluated using the example of defense and security based technological research. This is because the growing importance of this application field leads to an increasing number of research projects and to the appearance of many new technologies. [Copyright &y& Elsevier]

Abstract: This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization. [Copyright &y& Elsevier]

Abstract: Although several approaches have been proposed to specify multi-agent commitment-based protocols that capture flexible and rich interactions among autonomous and heterogeneous agents, very few of them synthesize their formal specification and automatic verification in an integrated framework. In this paper, we present a new logic-based language to specify commitment-based protocols, which is derived from ACTL∗c , a logic extending CTL∗ with modalities to represent and reason about social commitments and their actions. We present a reduction technique that formally transforms the problem of model checking ACTL∗c to the problem of model checking GCTL∗ (an extension of CTL∗ with action formulae). We prove that the reduction technique is sound and we fully implement it on top of the CWB-NC model checker to automatically verify the NetBill protocol, a motivated and specified example in the proposed specification language. We also apply the proposed technique to check the compliance of another protocol: the Contract Net protocol with given properties and report and discuss the obtained results. We finally develop a new symbolic algorithm to perform model checking dedicated to the proposed logic. [Copyright &y& Elsevier]

Abstract: In this paper we present a hybrid strategy developed using genetic algorithms (GAs), simulated annealing (SA), and quantum simulated annealing techniques (QSA) for the discrete time–cost trade-off problem (DTCTP). In the hybrid algorithm (HA), SA is used to improve hill-climbing ability of GA. In addition to SA, the hybrid strategy includes QSA to achieve enhanced local search capability. The HA and a sole GA have been coded in Visual C++ on a personal computer. Ten benchmark test problems with a range of 18 to 630 activities are used to evaluate performance of the HA. The benchmark problems are solved to optimality using mixed integer programming technique. The results of the performance analysis indicate that the hybrid strategy improves convergence of GA significantly and HA provides a powerful alternative for the DTCTP. [Copyright &y& Elsevier]

Abstract: Cell formation consists in organizing a plant as a set of cells, each of them containing machines that process similar types or families of parts. The idea is to minimize the part flow among cells in order to reduce costs and increase productivity. The literature presents different approaches devoted to solve this problem, which are mainly based on mathematical programming and on evolutionary computing. Mathematical programming can guarantee a global optimal solution, however at a higher computational cost than an evolutionary algorithm, which can assure a good enough optimum in a fixed amount of time. In this paper, we model and solve this problem by using state-of-the-art constraint programming (CP) techniques and Boolean satisfiability (SAT) technology. We present different experimental results that demonstrate the efficiency of the proposed optimization models. Indeed, CP and SAT implementations are able to reach the global optima in all tested instances and in competitive runtime. [Copyright &y& Elsevier]

Abstract: Condition-based maintenance has attracted an increasing attention both academically and practically. If the required physical models to describe the dynamic systems are unknown and the monitored information only reflects part of the state of the dynamic systems, expert knowledge is a source of valuable information to be used. However, expert knowledge is usually in a qualitative form, and therefore, needs to be transformed and combined with the measured characteristic information to provide effective prognosis. As such, this paper focuses on developing a novel approach to deal with the problem. In the proposed approach, a belief rule base (BRB) for the failure prognostic model is constructed using the expert knowledge and the analysis of the failure mechanism. An online failure prognostic algorithm is then proposed on the basis of the currently available characteristic variable information. The failure prognostic model is finally used in a condition based decision model to support the replacement decision of the dynamic systems. A case example is examined to demonstrate the implementation and potential applications of the proposed failure prognostic algorithm and the condition-based replacement model. [Copyright &y& Elsevier]

Abstract: The traditional particle filter algorithm cannot solve the validity of particles ideally. In this situation, a large amount of particles are required to guarantee tracking performance and tracking windows cannot change scale with targets adaptively. In addition, the particle filter algorithm which uses a single cue cannot achieve stable tracking regarding sudden illumination variation and similarly coloured background clutters. In this paper, an algorithm that combines CamShift with particle filter using multiple cues is proposed. The effectiveness of particles is improved and the tracking window can change scale with the target adaptively because of the use of CamShift. At the same time, an adaptive integration method is used to combine colour information with motion information, so the problems can be solved which are encountered in tracking an object with illumination variation and the background color clutter. Moreover, an occlusion handler is proposed to handle the full occlusion for a long time. [ABSTRACT FROM AUTHOR]

Abstract: Enhanced index tracking is a popular strategy in portfolio management that focuses on adding reliable value relative to the index on the basis of mimicking the behavior of the benchmark index. In this paper, we propose a multi-objective optimization scheme for the enhanced index tracking problem, which provides the framework of defining the objectives as both maximizing the degree of beating the benchmark index and minimizing the accumulated error of underperforming the benchmark. Transaction costs are limited in the constraints. An immunity-based multi-objective optimization algorithm is presented to search for the solution of the enhanced index tracking problem. Treatment of infeasibility and solution selection are also presented. Our proposed approach is implemented to five data sets drawn from major world markets. The computational results compared with other published results show that our method has superior performance. [ABSTRACT FROM AUTHOR]

Abstract: The permutation flow shop scheduling problem is addressed in this paper. Two objectives, minimization of makespan and total flow time, are considered. We propose a memetic algorithm, called NNMA, by integrating a general multiobjective evolutionary algorithm (NSGA-II) with a problem-specific heuristic (NEH). We take NEH as a local improving procedure in NNMA and propose several adaptations including the acceptance criterion and job-insertion ordering to deal with multiple objectives and to improve its performance. We test the performance of NNMA using 90 public problem instances with different problem scales, and compare its performance with 23 algorithms. The experimental results show that our NNMA provides close performance for 30 small-scale instances and better performance for 50 medium- and large-scale instances. Furthermore, more than 70% of the net set of non-dominated solutions is updated by NNMA for these 50 instances. [ABSTRACT FROM AUTHOR]

Abstract: Regularized logistic regression is a useful classification method for problems with few samples and a huge number of variables. This regression needs to determine the regularization term, which amounts to searching for the optimal penalty parameter and the norm of the regression coefficient vector. This paper presents a new regularized logistic regression method based on the evolution of the regression coefficients using estimation of distribution algorithms. The main novelty is that it avoids the determination of the regularization term. The chosen simulation method of new coefficients at each step of the evolutionary process guarantees their shrinkage as an intrinsic regularization. Experimental results comparing the behavior of the proposed method with Lasso and ridge logistic regression in three cancer classification problems with microarray data are shown. [ABSTRACT FROM AUTHOR]

Abstract: The goal of this research is to deduce important guidelines for designing effective Estimation of Distribution Algorithms (EDAs). These guidelines will enhance the designed algorithms in balancing the intensification and diversification effects of EDAs. Most EDAs have the advantage of incorporating probabilistic models which can generate chromosomes with the non-disruption of salient genes. This advantage, however, may cause the problem of the premature convergence of EDAs resulted in the probabilistic models no longer generating diversified solutions. In addition, due to overfitting of the search space, probabilistic models cannot really represent the general information of the population. Therefore, this research will deduce important guidelines through the convergency speed analysis of EDAs under different computational times for designing effective EDA algorithms. The major idea is to increase the population diversity gradually by hybridizing EDAs with other meta-heuristics and replacing the procedures of sampling new solutions. According to that, this research further proposes an Adaptive EA/G to improve the performance of EA/G. The proposed algorithm solves the single machine scheduling problems with earliness/tardiness cost in a just-in-time scheduling environment. The experimental results indicated that the Adaptive EA/G outperforms ACGA and EA/G statistically significant in different stopping criteria. This paper, hence, is of importance in the field of EDAs as well as for the researchers in studying the scheduling problems. [Copyright &y& Elsevier]

Abstract: In this paper, a particle swarm optimization (PSO) algorithm is presented to solve the single-machine scheduling problem with periodic maintenance activities. The most important issue for applying PSO algorithm successfully to the addressed scheduling problem is how to develop an effective ‘problem mapping’ and ‘construction of a particle sequence’ mechanism. For the problem mapping aspect, we apply the “job-to-position” representation for the particles. For the construction of a particle sequence aspect, we use the largest position value (LPV) rule. Besides, to enhance the effective of the proposed PSO algorithm, we embedded a restarting strategy and three stopping criteria. The objective is to find a schedule that minimizes the makespan. The addressed problem is shown to be NP-hard in the strong sense by transforming to the 3-partition problem. Computational results show that the proposed PSO-M algorithm is quite satisfactory on both solution accuracy and efficiency to solve the addressed problem. [Copyright &y& Elsevier]

Abstract: This paper proposes new Differential Evolution (DE) algorithm, named as Chaotically Initialized Differential Evolution (CIDE), that uses chaotic maps with certainty, ergodicity, unpredictability, non-periodicity, complex temporal behavior and the stochastic property for initial population generation. This has been done by using of different chaotic number generators each time a random number is needed by the classical DE algorithm in its initial step. Seven chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of DE and complex dynamics, can improve the quality of results in some optimization problems and also that chaos may be a desired process. It has been also shown that, Sinus map and Circle map have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions. [Copyright &y& Elsevier]

Abstract: Although new AI planning algorithms and techniques are being developed and improved rapidly, there is a lack of efficient and easy to use systems able to incorporate and utilize them. Furthermore, while visual representation facilitates design, maintenance and comprehension of planning domains and problems, very few systems incorporate it. This paper presents VLEPPO, an integrated system aiming at visually modeling planning domains and problems through a convenient graphical interface, while maintaining compatibility with the Planning Domain Definition Language (PDDL), with import and export features. Solutions to planning problems can be obtained by invoking different planners employing the web services technology. The demonstration of the system is performed through a case study involving web service composition viewed as a planning problem. [Copyright &y& Elsevier]

This paper introduces the algorithm portfolio concept to solve a combinatorial optimization problem pertaining to a supply chain. The supply chain problem is modeled with capacity constraints and demand variations over different time periods to minimize the total supply chain configuration cost. The algorithm portfolio is implemented over various problem instances to inspect and alleviate the computational expensiveness of a solution strategy. A bunch of five algorithms are utilized hereby viz. AIS, GA, Endosymbiotic Optimization, PSO and Psychoclonal algorithm. The observations reflect the appropriateness and effect of algorithm portfolios over the adopted supply chain, and viability over other optimization problems. [Copyright &y& Elsevier]

Recent research shows that simulated annealing with orthogonal array based neighbourhood functions can help in the search for a solution to a parametrical problem which is closer to an optimum when compared with conventional simulated annealing. Previous studies of simulated annealing analyzed only the main effects of variables of parametrical problems. In fact, both main effects of variables and interactions between variables should be considered, since interactions between variables exist in many parametrical problems. In this paper, an improved orthogonal array based neighbourhood function (IONF) for simulated annealing with the consideration of interaction effects between variables is described. After solving a set of parametrical benchmark function problems where interaction effects between variables exist, results of the benchmark tests show that the proposed simulated annealing algorithm with the IONF outperforms significantly both the simulated annealing algorithms with the existing orthogonal array based neighbourhood functions and the standard neighbourhood functions. Finally, the improved orthogonal array based simulated annealing was applied on the optimization of emulsified dynamite packing-machine design by which the applicability of the algorithm in real world problems can be evaluated and its effectiveness can be further validated. [Copyright &y& Elsevier]

Abstract: Recently, the Algorithmic Lateral Inhibition (ALI) method and the Accumulative Computation (AC) method have proven to be efficient in modelling at the knowledge level for general-motion-detection tasks in video sequences. More precisely, the task of persistent motion detection has been widely expressed by means of the AC method, whereas the ALI method has been used with the objective of moving objects detection, labelling and further tracking. This paper exploits the current knowledge of our research team on the mentioned problem-solving methods to model the Stereovision-Correspondence-Analysis (SCA) task. For this purpose, ALI and AC methods are combined into the Lateral Inhibition in Accumulative Computation (LIAC) method. The four basic subtasks, namely “LIAC 2D Charge-Memory Calculation”, “LIAC 2D Charge-Disparity Analysis” and “LIAC 3D Charge-Memory Calculation” in our proposal of SCA are described in detail by inferential CommonKADS schemes. It is shown that the LIAC method may perfectly be used to solve a complex task based on motion information inherent to binocular video sequences. [Copyright &y& Elsevier]