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One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]

This paper considers the queue-aware optimal energy minimisation sparse beamforming design (QESB) in a downlink cloud radio access network (C-RAN) system where multi-RRH communicates with multi-user through a central computing cloud via digital front-haul links. The problem is formulated as the joint optimisation problem of the transmission energy consumption, system queue length and front-haul cost with sparse beamforming design. As we know, the beamforming design adaptive to both QSI and CSI is challenging because of the high complexity. Apart from previous works that take queue length as constraints, in this paper we directly minimise the queue length state involved joint optimisation problem with SINR constraints. A smooth function is proposed to approximate the l0-norm function which is discrete and non-convex. To overcome the challenge due to the non-convexity of the optimisation problem, the semidefinite relaxation (SDR) technology is utilised to convert the primitive problem into the difference of convex (DC) programming problem, and convex and concave procedure (CCP) algorithm is used to induce the sparsity of the beamforming control. The simulation results show that the scheme proposed by this paper can obtain a good tradeoff between system energy consumption, queue length and front-haul cost with SINR constraints in C-RAN system. [ABSTRACT FROM AUTHOR]

Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR]

High penetration of Renewable Energy Sources into unbalanced distribution systems faces many challenges due to the uncertainty nature of both Renewable Energy Sources and loads as well as the unbalance degree of the distribution systems. This paper proposes the planning of Renewable Energy Sources in unbalanced distribution systems. Different types of Renewable Energy Sources, photo‐voltaic and Wind are considered. A multi‐objective optimisation problem is formulated using non‐dominated sort and crowing distance for multi objective hybrid algorithm that combines the merits of Particle Swarm Optimisation and Sine Cosine Optimisation algorithms. Three objectives, improving voltage profile, power losses minimisation, and minimisation of voltage unbalance, are considered. The proposed method determines the optimal Renewable Energy Sources' specifications such as site/size/type of distributed generators, number of photo‐voltaic modules or wind turbines in each distributed generator and their power factor and connection phases. Also, it determines the optimal setting of substation tap changer and system voltage regulators. Moreover, both Renewable Energy Sources and loads uncertainty are considered and modelled using Monte Carlo simulations. An improved matrix‐based backward–forward load flow method is developed by using coefficient matrices. The proposed procedure is conducted on IEEE 13‐node, 37‐node, and 123‐node unbalanced distribution systems. The proposed algorithm results compared with individual optimisers show significant performance improvements. The optimal plans resulted by using the proposed hybrid algorithm achieve a reduction in power losses that reached 89% in some cases. Also, the system unbalance index is reduced with a ratio reached 34%. Large improvement in the system voltages profile all over the year is achieved as the system voltage profile index is reduced by a ratio reached 70%. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is one of the efficient recent meta‐heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA‐VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA‐VNS is able to provide very competitive results and outperforms the other algorithms. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of this paper is to introduce a novel dynamic multi‐objective optimization algorithm. First, after detecting the environmental changes, Borda count ranking method is applied to population in order to assign the Borda score to each individual, and then the lowest score individuals are removed from population and replaced with new created solutions. Furthermore, fuzzy adaptive multi‐objective cat swarm optimization algorithm is used to estimate the Pareto‐optimal front in which its parameters are tuned to new environment by Mamdani fuzzy rules when a change occurs. Performance of the proposed algorithm is tested on dynamic multi‐objective benchmarks and is compared with recent achievements. The simulations show the quite satisfactory results and higher performance of the proposed method in comparison with traditional approaches. [ABSTRACT FROM AUTHOR]

In the existing mobile edge computing (MEC) environment, edge servers have limited computing power and cannot process data in real time. To solve this problem, this paper proposes a task segmentation and computing offloading algorithm for mobile edge computing (MEC). Firstly, the computing tasks of mobile terminals are segmented under the network framework of cloud‐side‐device collaboration, and a task offloading optimization model is designed to minimize the total delay of mobile devices. Secondly, the computing resource allocation parameters and action space parameters are discretized, and a computing offloading algorithm based on Deep Q Network (DQN) is proposed. The algorithm extracts feature from high‐dimensional data and uses deep neural network to estimate Q‐value of actions to maximize the summation of system delays. Finally, simulation experiments show that when the cloud computing capacity is 7 × 1011 central processing unit (CPU) cycle/s, the total delay of proposed algorithm is only 1.116 s, while the total delays of comparison algorithms are 1.251 s and 1.193 s, respectively. Therefore, compared with the comparison algorithm, the proposed algorithm can better reduce the total system delay. [ABSTRACT FROM AUTHOR]

Summary: Computational intelligence techniques have widespread applications in the field of engineering process optimization, which typically comprises of multiple conflicting objectives. An efficient hybrid algorithm for solving multi‐objective optimization, based on particle swarm optimization (PSO) and artificial bee colony optimization (ABCO) has been proposed in this paper. The novelty of this algorithm lies in allocating random initial solutions to the scout bees in the ABCO phase which are subsequently optimized in the PSO phase with respect to the velocity vector. The last phase involves loyalty decision‐making for the uncommitted bees based on the waggle dance phase of ABCO. This procedure continues for multiple generations yielding optimum results. The algorithm is applied to a real life problem of intercity route optimization comprising of conflicting objectives like minimization of travel cost, maximization of the number of tourist spots visited and minimization of the deviation from desired tour duration. Solutions have been obtained using both pareto optimality and the classical weighted sum technique. The proposed algorithm, when compared analytically and graphically with the existing ABCO algorithm, has displayed consistently better performance for fitness values as well as for standard benchmark functions and performance metrics for convergence and coverage. [ABSTRACT FROM AUTHOR]

A mobile robot is an autonomous agent, capable of planning the path from source to destination in both, a known, or an unknown environment. In this paper, we presented a novel approach to find the optimal trajectory. We have hybridized oppositional‐based learning (OBL) with the evolutionary invasive weed optimization (IWO) technique to generate the optimal path for different robot(s). The navigation algorithm considered in this work is intelligent enough to fulfill the objective of minimizing the path length and the time to reach its specified goal. Oppositional IWO (OIWO) algorithm mimics the colonizing behavior of weed and uses the concept of quasi‐opposite number, the concept of OBL is to find the shortest path between source and destination of mobile robot(s). An objective function has been formulated that takes care of the optimal target‐seeking behavior as well as the obstacle avoidance of the mobile robot. In this technique, OBL considers the current population generated by IWO algorithm and its opposite population simultaneously to get better and faster convergence. The simulation and experimental results prove and validate the effectiveness of developed OIWO path planning algorithm. [ABSTRACT FROM AUTHOR]

Stochastic gradient descent algorithm is a classical and useful method for stochastic optimisation. While stochastic gradient descent has been theoretically investigated for decades and successfully applied in machine learning such as training of deep neural networks, it essentially relies on obtaining the unbiased estimates of gradients/subgradients of the objective functions. In this paper, by constructing the randomised differences of the objective function, a gradient‐free algorithm, named the stochastic randomised‐difference descent algorithm, is proposed for stochastic convex optimisation. Under the strongly convex assumption of the objective function, it is proved that the estimates generated from stochastic randomised‐difference descent converge to the optimal value with probability one, and the convergence rates of both the mean square error of estimates and the regret functions are established. Finally, some numerical examples are prsented. [ABSTRACT FROM AUTHOR]

Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works. [ABSTRACT FROM AUTHOR]

Plant leaves play an important role in the diagnosis of plant diseases. Losses from such diseases can have a significant economic as well as environmental impact. Thus, examination of leaves into a healthy or infected carries substantial importance. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private dataset of 236 images. Features are extracted from the images using histogram of oriented gradients (descriptor). The concepts of artificial plant optimization are then applied to study the features of healthy leaves using IAPO. A machine learning algorithm has been created to make the model adaptive with varied datasets. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. The proposed IAPO is also compared with other classification algorithms including k‐nearest neighbours, support vector machine, random forest and convolution neural network that show accuracies of 78.24%, 83.48%, 87.83%, and 91.26%, respectively, whereas IAPO shows quite accurate results in classification of leaves with an accuracy of 97.45% on training set and 95.0% accuracy on test set. [ABSTRACT FROM AUTHOR]

This paper demonstrates how branch-and-bound algorithms can be modularized to obtain implementation efficiencies. For the manager, this advantage can be used to obtain faster implementation of algorithm results; for the scientist, it allows efficiencies in the construction of similar algorithms with different search and addressing structures for the purpose of testing to find a preferred algorithm. The demonstration in part is achieved by showing how the computer code of a central module of logic can be transported between different algorithms that have the same search strategy. Modularizations of three common searches (the best-bound search and two variants of the last-in-first-out search) with two addressing methods are detailed and contrasted. Using four assembly line balancing algorithms as examples, modularization is demonstrated and the search and addressing methods are contrasted. The application potential of modularization is broad and includes linear programming based integer programming. Benefits and disadvantages of modularization are discussed. Computational results demonstrate the viability of the method. [ABSTRACT FROM AUTHOR]

Here, the multi-objective optimisation algorithm speed-constrained multi-objective particle swarm optimisation is applied to the synthesis of uniformly excited sparse-rectangular planar array with low sidelobe level. Numerical simulations have been conducted to test the performance of multi-objective optimisation algorithm when used in this synthesis problem, and resulting radiation patterns are compared with those from other existing synthesis algorithms in literatures. Comparative results illustrate that the proposed algorithm has obtained the lowest maximum sidelobe level (MSLL) in φ = 0 plane (φ is the elevation angle) of radiation pattern combined with the lowest maximum MSLL in φ = π/2 plane. In addition, convergence performance of the multi-objective optimisation algorithm when used in this synthesis problem has also been investigated here. [ABSTRACT FROM AUTHOR]

Supply chain formation is the process by which a set of producers within a network determine the subset of these producers able to form a chain to supply goods to one or more consumers at the lowest cost. This problem has been tackled in a number of ways, including auctions, negotiations, and argumentation-based approaches. In this paper we show how this problem can be cast as an optimization of a pairwise cost function. Optimizing this class of energy functions is NP-hard but efficient approximations to the global minimum can be obtained using loopy belief propagation (LBP). Here we detail a max-sum LBP-based approach to the supply chain formation problem, involving decentralized message-passing between supply chain participants. Our approach is evaluated against a well-known decentralized double-auction method and an optimal centralized technique, showing several improvements on the auction method: it obtains better solutions for most network instances which allow for competitive equilibrium (Competitive equilibrium in Walsh and Wellman is a set of producer costs which permits a Pareto optimal state in which agents in the allocation receive non-negative surplus and agents not in the allocation would acquire non-positive surplus by participating in the supply chain) while also optimally solving problems where no competitive equilibrium exists, for which the double-auction method frequently produces inefficient solutions. [ABSTRACT FROM AUTHOR]

In this paper, Bayesian-Networks (BN) and Ant Colony Optimization (ACO) techniques are combined to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work is that the amount of pheromones released is variable. This amount is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required. By using ACO and BN, a fitness function, responsible of automatically selecting the next course in the learning graph, is defined. This is done by generating a path that maximizes the probability of each user's success in the course. Therefore, the path can change to improve learners' average performance, taking into account the pedagogical weight of each learning unit and the social behavior of the system. Furthermore, a discrete dynamical system is obtained and its stability is studied. How to wrap an existing Learning Management System is also described in this work. Finally, an experiment compares this approach with the old on-line learning system being used previously. [ABSTRACT FROM AUTHOR]

A fast dynamic programming technique based on a fuzzy based unit selection procedure is proposed in this paper for the solution of the unit commitment problem with ramp constraints. The curse of dimensionality of the dynamic programming technique is eliminated by minimizing the number of prospective solution paths to be stored at each stage of the search procedure. Heuristics like priority ordering of the units, unit grouping, fast economic dispatch based on priority ordering, and avoidance of repeated economic dispatch through memory action have been employed to make the algorithm fast. The proposed method produced comparable results with the best performing methods found in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M-estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the ‘Aspect problem’ in quantum physics. [ABSTRACT FROM AUTHOR]

Ontology matching is a process for selection of a good alignment across entities of two (or more) ontologies. This can be viewed as a two-phase process of (1) applying a similarity measure to find the correspondence of each pair of entities from two ontologies, and (2) extraction of an optimal or near optimal mapping. This paper is focused on the second phase and introduces our evolutionary approach for that. To be able to do so, we need a mechanism to score different possible mappings. Our solution is a weighting mechanism named coincidence-based weighting. A genetic algorithm is then introduced to create better mappings in successive iterations. We will explain how we code a mapping as well as our crossover and mutation functions. Evaluation of the algorithm is shown and discussed. [ABSTRACT FROM AUTHOR]

Preference elicitation is a serious bottleneck in many decision support applications and agent specification tasks. Ceteris paribus (CP)-nets were designed to make the process of preference elicitation simpler and more intuitive for lay users by graphically structuring a set of CP preference statements—preference statements that most people find natural and intuitive. Beside their usefulness in the process of preference elicitation, CP-nets support efficient optimization algorithms that are crucial in most applications (e.g., the selection of the best action to execute or the best product configuration). In various contexts, CP-nets with an underlying cyclic structure emerge naturally. Often, they are inconsistent according to the current semantics, and the user is required to revise them. In this paper, we show how optimization queries can be meaningfully answered in many “inconsistent” networks without troubling the user with requests for revisions. In addition, we describe a method for focusing the user's revision process when revisions are truly needed. In the process, we provide a formal semantics that justifies our approach and new techniques for computing optimal outcomes. Some of the methods we use are based on a reduction to the problem of computing stable models for nonmonotonic logic programs, and we explore this relationship closely. [ABSTRACT FROM AUTHOR]

Traditional approaches for modeling economic production lot-sizing problems assume that a single, fixed equipment setup cost is incurred each time a product is run, regardless of the quantity manufactured. This permits multiple days of production from one production setup. In this paper, we extend the model to consider additional fixed charges, such as cleanup or inspection costs, that are associated with each time period's production. This manufacturing cost structure is common in the food, chemical, and pharmaceutical industries, where process equipment must be sanitized between item changeovers and at the end of each day's production. We propose two mathematical problem formulations and optimization algorithms. The models' unique features include regular time production constraints, a fixed charge for each time period's production, and the availability of overtime production capacity. Experimental results indicate the conditions under which our algorithms' performance is superior to traditional approaches. We also test the procedures on a set of lot-sizing problems facing a national food processor and document their potential economic benefit. [ABSTRACT FROM AUTHOR]

This paper presents and solves a model for the multiple supplier inventory grouping problem, which involves the minimization of logistics costs for a firm that has multiple suppliers with capacity limitations. The costs included in the model are purchasing, transportation, ordering, and inventory holding, while the firm's objective is to determine the optimal flows and groups of commodities from each supplier. We present an algorithm, which combines subgradient optimization and a primal heuristic, to quickly solve the multiple supplier inventory grouping problem. Our algorithm is tested extensively on problems of various sizes and structures, and its performance is compared to that of OSL, a state-of-the-art integer programming code. The computational results indicate that our approach is extremely efficient for solving the multiple supplier inventory grouping problem. [ABSTRACT FROM AUTHOR]

Some vendors offer their products to a purchaser in various container sizes. Normally, vendors will offer larger all-units discounts on larger container sizes. Two algorithms have been developed in this paper to solve for the optimal number of each container size and the order quantity for the deterministic economic order quantity (EOQ) situation. The first algorithm, based in part on the dynamic programming solution to knapsack problems, solves the general case. The second algorithm, based in part on the dominance relations inherent in the pricing schedule, solves the more restricted case in which the purchaser is forced to accept the discount in the following manner. An order must be filled with containers of successively smaller sizes, beginning with the largest size. The purchaser is forced to accept the largest discount first, then the next largest, and so on. [ABSTRACT FROM AUTHOR]

The expected geometric mean has been shown to be a valuable criterion in ranking portfolios of assets. For example, under certain conditions the maximum expected geometric-mean portfolio minimizes the expected time to amass a fixed level of wealth as wealth becomes "large." This paper develops a simple algorithm for obtaining such portfolios when portfolio returns are assumed to be lognormally distributed. The risk-return characteristics of portfolios are derived and illustrated using data from a subset of stocks found on the New York Stock Exchange. [ABSTRACT FROM AUTHOR]

There has been a growing interest in the cooperative communication of power line and wireless dual‐medium, due to the promising transmission quality and immense flexibility of the network. However, under the condition of hybrid fading and impulsive noise, the theoretical analysis of two‐way relaying communication with power line and wireless is difficult because of the lack of closed analytical expressions. In this study, the authors propose a relaying signal processing method based on Log Likelihood Ratio information estimation for two‐way relaying systems; and a power allocation optimisation algorithm based on Q‐learning, which can adaptively select the appropriate transmission power to maximise the mutual information. More precisely, the ϵ ${\epsilon}$‐greedy strategy is adopted for the estimated forwarding protocol and the transmission power of each node is adjusted to determine the optimal power allocation factor. The performances such as mutual information and outage probability are analysed and compared and the simulation shows that the algorithm is reliable and effective. [ABSTRACT FROM AUTHOR]

A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective. [ABSTRACT FROM AUTHOR]

In this study, the joint design of discrete phase transmit sequences and receive filters with the low peak sidelobe level and integrated sidelobe level in radar systems is considered. The problem is formulated as a minimisation of the peak (integrated) sidelobe level, forcing a unimodular discrete phase constraint at the design stage. To deal with the resulting non‐convex and in general the NP‐hard optimisation problem, an iterative method based on the alternate optimisation approach is proposed. Each iteration of the proposed method involves a non‐convex min–max problem to be solved. In addition, a sequential rank‐one constraint relaxation framework is proposed to initialise the procedure, each iteration of which requires the solution of a convex problem. Simulation results show that using the proposed framework for jointly finding transmit sequences and receive filters leads to a better peak sidelobe ratio and integrated sidelobe ratio for discrete phase sequences than the optimal mismatched filters design approach. [ABSTRACT FROM AUTHOR]

Retrieving the relevant information from the high‐dimensional dataset enhances the classification accuracy of a predictive model. This research critique has devised an improved marine predator algorithm based on opposition learning for stable feature selection to overcome the problem of high‐dimensionality. Marine predator algorithm is a population‐based meta‐heuristics optimization algorithm that works on the 'survival‐of‐the‐fittest' theory. Classical marine predator algorithm explores the search space merely in one direction, affecting its converging capacity while being responsible for stagnation at local minima. The proposed opposition‐based learning nuances enhance the exploration capacity of marine predator algorithm and productively converges the model to global optima. The proposed OBL‐based marine predator algorithm selects stable, substantial elements from six different high‐dimensional microarray datasets. The performance of the proposed method is investigated using five predominantly used classifiers. From the result, it is understood that the proposed approach outperforms other conventional feature selection techniques in terms of converging capability, classification accuracy, and stable feature selection. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) and the whale optimization algorithm (WOA) are two admired optimization methods that have drawn various researchers' attention. The PSO implements some particles' intelligent movements in a search space, and the WOA is originated based on the hunting mechanism of humpback whales. The PSO and WOA have different strategies for moving towards the optimum solution. Nevertheless, both algorithms' performances encounter several problems, such as premature convergence and falling in local optimums. Several approaches have been proposed to enhance meta‐heuristic algorithms' performance, such as applying the chaotic maps, adding mathematical or stochastic operators or local searches, and hybridizing the algorithms. In this article, a new hybrid algorithm denoted as chaotic‐based hybrid whale and PSO has been presented by improving the WOA, combining it with PSO, and using the chaotic maps. The hybrid algorithm has significantly more diverse movements than both of the mentioned algorithms. Therefore, it explores different regions of a problem's search space more precisely and avoids local optima. The roulette wheel selection operator has also been applied based on their fitness value to select the proposed algorithm's search agents and exploit promising regions of the search space. In the hybrid algorithm, the chaotic maps have been applied to initialize the whales' population, particles of the particle swarm, and adjust motion parameters to increase population diversity. The multi‐swarm version of the proposed algorithm with higher performance than the single‐swarm version and other methods has been introduced in this article too. The proposed algorithms have been evaluated using 23 mathematical benchmark functions, including unimodal, multimodal, and composite functions and four engineering optimization problems. The obtained results and statistical tests prove that the proposed algorithms provide competitive solutions for most of the experiments, compared to the state‐of‐the‐art and well‐known optimization meta‐heuristic methods in terms of convergence towards the global optimum, local optima avoidance, exploration, and exploitation. [ABSTRACT FROM AUTHOR]

Long-term monitoring of fetal heart is vital for early clinical diagnosis and timely treatment of fetus. However, the collected signals from the abdomen of the pregnant woman are always corrupted by many interfering sources, most significantly affected by the maternal electrocardiograph (ECG). Similar with blind source separation (BSS), fetal ECG extraction can be considered as the separation of fetal ECG from the recordings. This letter proposes a novel multi-layer polynomial network to extract the fetal ECG waveform, where the non-linear mixing process is approximated with multiple polynomials. The network starts with the first-order polynomial layer, and further requirements to decrease the error between the output and target signal is achieved by increasing the number of layers. The proposed approach turns the inverse transformation of traditional BSS into a model learning problem, which is then solved by a simple convex optimization algorithm. Compared with the state-of-the-art BSS methods, experimental results show that the proposed method has better performance. [ABSTRACT FROM AUTHOR]

The paper reformulates Houthakker's capacity method for quadratic programming in the framework of the simplex and dual methods for quadratic programming, thereby greatly reducing the conceptual and computational complexities of the method. It is shown that the method is applicable for all convex quadratic programming problems, including the case of a semi-definite matrix of the quadratic form and that of constraints in equality form. In the linear programming case the method reduces to a parametric version of the dual method. [ABSTRACT FROM AUTHOR]

For the scenario where the overall layout is known and the obstacle distribution information is unknown, a dynamic path planning algorithm combining the A* algorithm and the proximal policy optimization (PPO) algorithm is proposed. Simulation experiments show that in all six test environments, the proposed algorithm finds paths that are on average about 2.04% to 5.86% shorter compared to the state‐of‐the‐art algorithms in the literature, and reduces the number of training epochs before stabilization from tens of thousands to about 4000. [ABSTRACT FROM AUTHOR]

This study deals with the error probability optimisation problem in three-phase bidirectional amplify and forward relaying systems. Different from the common two-phase strategy, in the three-phase counterpart, the relay power is split into two different terminals in terms of data error probability. With such a three-phase division, the authors present a unified optimisation algorithm based on the exact symbol error probability (SEP) and its upper bound expression. In addition, by virtue of the dual decomposition algorithm, the authors derive the optimal power allocation and demonstrate its uniqueness at both terminals and the relay node. Moreover, the unified optimisation algorithm can be reduced to some special cases, such as the maximum minimisation problem and unbalanced link problem. The proposed results reveal that the proposed algorithm has a significant impact on SEP reduction. [ABSTRACT FROM AUTHOR]

Features selection is the process of choosing the relevant subset of features from the high‐dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high‐dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble‐based algorithms such as support vector machine (SVM), K‐nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR‐FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%. [ABSTRACT FROM AUTHOR]

Summary: Ensemble learning is the process of aggregating the decisions of different learners/models. Fundamentally, the performance of the ensemble relies on the degree of accuracy in individual learner predictions and the degree of diversity among the learners. The trade‐off between accuracy and diversity within the ensemble needs to be optimized to provide the best grouping of learners as it relates to their performance. In this optimization theory article, we propose a novel ensemble selection algorithm which, focusing specifically on clustering problems, selects the optimal subset of the ensemble that has both accurate and diverse models. Those ensemble selection algorithms work for a given number of the best learners within the subset prior to their selection. The cardinality of a subset of the ensemble changes the prediction accuracy. The proposed algorithm in this study determines both the number of best learners and also the best ones. We compared our prediction results to recent ensemble clustering selection algorithms by the number of cardinalities and best predictions, finding better and approximated results to the optimum solutions. [ABSTRACT FROM AUTHOR]

By increase in smart phone penetration rate, mobile social networks (MSNs) become more popular. In such networks, users can exchange and share information via peer-to-peer opportunistic wireless connections. Wireless connections are prone to failures, devices are battery-powered, and the buffer space is limited. These lead to uncertainty in connections and selfish behaviours in dissemination processes. Hence, information dissemination in MSNs becomes a challenge. In this study, the authors analyse the information dissemination in MSNs with selfish users from different communities. They develop an analytical model through ordinary differential equations to analyse the dissemination process in MSNs. Then, they propose an optimisation problem to find the optimal forwarding probabilities of users. They employ the branch and bound-outer approximation algorithm to analytically solve the optimisation problem. The analytical results represent that the optimal forwarding probability of users diminished by increasing the number of relay users, which accelerate the dissemination in the network. Also, these results represent that the proposed algorithm to find the optimal selfishness vector can improve the network performance by decreasing the dissemination delay. [ABSTRACT FROM AUTHOR]

Current approaches to information-centric network (ICN) implementation employ the edge storage of network infrastructures to minimise the latency of information retrieval. However, the edge storage of infrastructures is finite. Distributed content caching assisted by mobile devices becomes a potential solution to content publishing services. Nevertheless, under the conventional half-duplex-based ICN, extra memory is occupied and the delay is increased. Fortunately, full duplex (FD) communications can improve the spectrum efficiency of wireless networks, and reduce the access delay by more flexible content caching. In this study, the authors propose a novel FD-based ICN (FD-ICN) framework, where content caching is not only implemented in network infrastructures but also in mobile devices. Content cached by mobile devices can be delivered in a FD amplify and forward way without extra BS memory occupation and delay. To maximise the network utility of the FD-ICN, they formulate the caching strategy and FD radio resource allocation as a joint optimisation problem. A joint caching strategy and FD radio resource allocation algorithm are proposed. Simulation results show their FD-ICN framework can achieve superior system utility compared to the conventional ICN. Moreover, the proposed algorithm exhibits higher utility with similar converge rate compared to the heuristic algorithm. [ABSTRACT FROM AUTHOR]

Morton and Wecker (1977) stated that the value iteration algorithm solves a dynamic program's policy function faster than its value function when the limiting Markov chain is ergodic. I show that their proof is incomplete, and provide a new proof of this classic result. I use this result to accelerate the estimation of Markov decision processes and the solution of Markov perfect equilibria. [ABSTRACT FROM AUTHOR]

Markov chain Monte Carlo algorithms generate samples from a target distribution by simulating a Markov chain. Large flexibility exists in specification of transition matrix of the chain. In practice, however, most algorithms used only allow small changes in the state vector in each iteration. This choice typically causes problems for multi-modal distributions as moves between modes become rare and, in turn, results in slow convergence to the target distribution. In this paper we consider continuous distributions on R[sup n ] and specify how optimization for local maxima of the target distribution can be incorporated in the specification of the Markov chain. Thereby, we obtain a chain with frequent jumps between modes. We demonstrate the effectiveness of the approach in three examples. The first considers a simple mixture of bivariate normal distributions, whereas the two last examples consider sampling from posterior distributions based on previously analysed data sets. [ABSTRACT FROM AUTHOR]

In this study, a closed-form solution is presented for the constrained water-filling problem (CWFP) where in powers allocated to the user resources maximise the user's capacity under an interference constraint and a total power constraint. Total power constraint in CWFP is to save the energy in wireless network. To solve CWFP, erstwhile algorithms compute powers for all the resources iteratively. Unlike existing algorithms, the proposed method calculates the number of resources that gets positive (or non-zero) powers using the concepts of traditional water-filling problem. Later, powers are allocated to the resolved resources (that gets positive powers) alone using the closed-form solution, which reduces the number of computations. It can be discerned that the computational complexity of the given solution is of the order of NS where NS is the total number of resources; which is remarkably lower than that of the prior algorithms specified by an order of 103 where NS is the total number of resources; which is remarkably lower than that of the prior algorithms specified by an order of 105 × NS 3 . [ABSTRACT FROM AUTHOR]

Mobile ad-hoc network (MANET) is an emerging technology that comes under the category of wireless network. Even though the network assumes that all its mobile nodes are trusted, it is impossible in the real world as few nodes may be malicious. Therefore, it is essential to put forward a mechanism that can provide security by selecting an optimal route for data forwarding. In this study, a goal programming model is designed using a hybrid optimisation algorithm, called M-LionWhale, for secure routing. M-LionWhale is an optimisation algorithm that incorporates lion algorithm (LA) into whale optimisation algorithm (WOA) for the optimal selection of the path in MANET. The multi-objective optimisation model considers several quality of service (QoS) parameters, namely energy, distance, link lifetime, delay, and trust. With the estimated multi-objective parameters, a fitness function is developed for the best selection of routes. The performance of the proposed algorithm is evaluated using three metrics, such as packet delivery ratio (PDR), throughput, and energy and is compared with that of existing trust-based QoS routing model, LA, and WOA. The proposed M-LionWhale algorithm could attain the maximum performance with 24.1313% residual energy, throughput of 0.2966 kbps, and PDR of 0.3051 at maximum simulation time. [ABSTRACT FROM AUTHOR]

In this work, the exponential weighted square-of-error functions are exploited for controller design. It is reported that the exponential weight in square-of-error objective function is related to the maximum sensitivity of the process. Therefore, it is possible to control the maximum sensitivity of the process without measuring the frequency response. Moreover, the existing integral-square-error- and integral-time-square-error-based proportional-integral-derivative (PID) tuning rule is modified with the proposed exponential weighted square-of-error functions that allow the user to further tune the maximum sensitivity of the process. The proposed theory is corroborated with the help of a gain optimisation example by the traditional minimisation approach and a PID controller design example by the proposed tuning rule (for the stable/unstable process) and stochastic optimisation approach. [ABSTRACT FROM AUTHOR]

Rational partitioning method of power grid is the premise and foundation to implement wide-area backup protection with multi-source information. This study shows the universal structure of a wide-area backup protection system based on regional centralised decision-making mode, and presents a multi-objective optimisation method of power grid partitioning. Under the premise of reliable protection, a multi-objective function is built, comprehensively considering the communication performance, overlap rate, and the balance of regional workloads. To obtain the optimal result, the brain storm optimisation (BSO) algorithm is used in this study. The method is applied on IEEE 11-bus and 39-bus test systems and simulated data is used to verify the effectiveness and feasibility of the optimisation method. [ABSTRACT FROM AUTHOR]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The energy harvesting paradigm enables the design of new wireless powered cellular networks (WPCNs) for the Internet-of-Things (IoT). In a WPCN, if the harvested energy is larger than the consumed energy, an IoT device may utilise the surplus harvested energy to extend the battery lifetime or to enhance the performance. Otherwise, a power saving mechanism is utilised as in traditional wireless cellular networks. In long-term evolution (LTE) systems, the discontinuous reception (DRX) mechanism has been adopted to save the power consumption of user equipments (UEs). However, the wakeup delay increases as the amount of the saved energy increases, and the optimisation for the DRX mechanism is required. In this study, the authors propose a novel energy harvesting DRX (EH-DRX) mechanism to minimise the wakeup delay in WPCNs. In the authors' proposed EH-DRX mechanism, an IoT device utilises the harvested energy to minimise the wakeup delay. Then they provide an optimisation algorithm to allocate the DRX parameters by utilising the harvesting capability of IoT devices. The optimisation algorithm is based on the analytical model for the proposed EH-DRX mechanism. Analysis and simulations indicate that the proposed EH-DRX mechanism reduces the wakeup delay compared to the traditional DRX mechanisms. [ABSTRACT FROM AUTHOR]

In this study, applied research based on sub-band adaptive filtering (SAF) of electroencephalography/event related potential signal has been conducted. Noisy co-variants of event related potential signals are filtered through sub-band adaptive filters (AFs). SAF with evolutionary techniques (ETs) has been attempted first time. Five ETs, such as particles swarm optimisation, artificial bee colony, cuckoo optimisation algorithm, genetic algorithm, and differential evolution with their variants have been employed for optimisation of SAF. Average computational time and shape measures the difference of ET-based sub-AFs have been improved by decimation in sub-bands before adaptive filtering. [ABSTRACT FROM AUTHOR]

This article describes a coordinated ramp metering algorithm for systematically mitigating freeway congestion. A preemptive hierarchical control scheme with a three-priority-layer structure is employed in this algorithm. Ramp metering is formulated as a multiobjective optimization problem to enhance system performance. These optimization objectives include promptly tackling freeway congestion, sufficiently utilizing on-ramp storage capacities, and preventing on-ramp vehicles from overflowing to local streets, balancing on-ramp vehicle equity, and maximizing traffic throughputs for the entire system. Instead of relying heavily on accurate estimates of freeway traffic flow evolvement, this new approach models ramp meter control as a linear program and uses real-time traffic sensor measurements for minimizing the indeterminate impacts from the mainstream flow capacities. VISSIM-based simulation experiments are performed to examine its practicality and effectiveness using geometric and traffic demand data from one real-world freeway segment. The simulation test results show that the proposed ramp metering approach performed well in optimizing overall freeway system operations under various traffic conditions. The system-wide optimal control performance can be achieved to quickly mitigate freeway congestion, prevent traffic from overflowing to local streets, and maximize overall traffic throughputs. The proposed ramp metering approach can dynamically assemble relevant ramp meters to work together and effectively coordinate the individual meter rates to leverage their response strengths for minimizing time to clear the congestion. This study demonstrates that utilization of existing freeway infrastructure can be optimized through the proposed algorithm. [ABSTRACT FROM AUTHOR]

We propose an ℓ-penalized estimation procedure for high-dimensional linear mixed-effects models. The models are useful whenever there is a grouping structure among high-dimensional observations, that is, for clustered data. We prove a consistency and an oracle optimality result and we develop an algorithm with provable numerical convergence. Furthermore, we demonstrate the performance of the method on simulated and a real high-dimensional data set. [ABSTRACT FROM AUTHOR]

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

Copyright of Canadian Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)