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This paper proposes a scheduling strategy for irregular curved blocks to address the complex spatiotemporal coupling scheduling problem related to the entered time, the entered sequence, the setting positions and the rotated angles for the curved blocks in a shipbuilding yard. The strategy presents a makespan-based curved blocks - classification and selection rule to fulfil the programming time for the entry of the curved blocks into the workplace and realises the suppression on the delay. Useless stepping search of curved blocks in occupied workplace is avoided by combining the lowest centre-of-gravity rule with the calculation method of the remained workplace proposed in this paper. A modified genetic ant colony algorithm was proposed, which apply the ease to premature characteristics of GA and the excellent local optimisation ability of ACO, to let and promote the algorithm falls into local optimum. Then the large-scale and full-range mutation will be implemented to make the algorithm jump out of the original local optimisation to search more local optimal solutions so that the global optimal solution can be achieved. Finally, a software system for algorithm verification was developed which conducts the comparative analysis of the algorithms and verifies the validity of the algorithm proposed. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

Exponentially weighted moving average (EWMA) control charts are well-established devices for monitoring process stability. Typically, control charts are evaluated by considering their Average Run Length (ARL), that is the expected number of observations or samples until the chart signals. Because of the limitations of an average, various papers also dealt with the run length distribution and quantiles. Going beyond these papers, we develop algorithms for and evaluate the quantile performance of EWMA control charts with variance adjusted control limits and with fast initial response features, of EWMA charts based on the sample variance, and of EWMA charts simultaneously monitoring mean and variance. Additionally, for the mean charts we consider medium, late and very late process changes and their impact on appropriately conditioned run length quantiles. It is demonstrated that considering run length quantiles can protect from constructing distorted EWMA designs while optimising theirzero-stateARL performance. The implementation of all the considered measures in the R package ‘spc’ allows any control chart user to consider EWMA schemes from the run length quantile prospective in an easy way. [ABSTRACT FROM PUBLISHER]

Twenty-first century is an era of cutting-edge technologies. From artificial intelligence to nanotechnology, modern life has gathered the pace to match up with the shrinking time and fast-moving world. Likewise, a Heterogeneous Network finds its own space in this century of inventions. Every individual, industry, and institute needs to be connected to the internet cloud. Mobile applications have changed the dynamics of every individual's life. From personal management to professional proficiency, mobile applications play an important role in almost every aspect. Numerous applications available these days demand continuous bandwidth connection. To remain always connected, there is a need for an effective handoff procedure. In this paper, we have proposed a convex optimization technique for optimizing the call drop probability, remaining energy and handoffs in heterogeneous networks. The proposed algorithm outperformed the already existing M-OPTG and M-WO algorithms. [ABSTRACT FROM AUTHOR]

Smart device networks form the backbone of many Internet of Things (IoT) applications. The performance of these systems depended on many factors. This includes their ability to connect to each other and network latency. In this paper, two algorithms are discussed that build a Peer-to-peer (P2P) overlay network. The focus is on real-time end-to-end remote instrumentation for IoT applications. P2P overlay networks are used for various purposes from content distribution through to media sharing. In contrast to traditional P2P systems, IoT systems can be highly sensitive to network latency and require low bandwidth. The main contributions of this paper are two algorithms to create overlay networks that assign peer nodes to super-peer nodes. The network contains a large number of smart devices and the key aim is to minimize system wide network latency. Other constraints regarding the nodes ability include the ability to become a super-peer node such as connectivity and limited processing capacities of the smart devices are also considered in context of a P2P remote instrumentation system. Both algorithms create a 3-hop network where the latency is lowered as much as possible with the given set of nodes. The algorithms have been validated through simulation. [ABSTRACT FROM AUTHOR]

The assembly line balancing problem can completely vary from one production line to the other. This paper deals with a realistic assembly line for the automotive industry inspired by Fiat Chrysler Automotive in North America and Parskhodro in Iran (both large-scale automotive companies). This problem includes some specific requirements that have not been studied in the literature. For example, the assembly line is five-sided, and workers can move along these sides. Due to the limited workspace, all the sides cannot work simultaneously at one station. First, a mixed integer linear programming model is proposed for the problem. Then, the model is improved to have a tighter linear relaxation. Moreover, an effective logic-based Benders' decomposition algorithm is developed. After careful analysis of problem's structure, three propositions are introduced. The master problem is well restricted by eight valid inequalities. Two different sub-problem types are defined to extract more information from the master problem's solution. In this case, the algorithm adds effective cuts that reduce the solution space to the extent possible at each iteration. Thus, the number of iterations is significantly cut down. The performance of the model and algorithm, as well as improvement made on both, is evaluated. [ABSTRACT FROM AUTHOR]

Power distribution is one of the major areas of electrical engineering. The issue of optimized power distribution is of great concern and here it is dealt with as a single- and multi-objective problem. We know that evolutionary algorithms have better efficiency in solving such problems. In this paper, we have applied heuristics non-dominated sorting genetic algorithm II (NSGA-II, multiple objective optimization algorithm) to optimize functions such as corona loss, efficiency, potential drop, resistive loss, and volume of the conductor. The NSGA-II has outperformed other algorithms involving the optimal solution. NSGA-II is not only simple in terms of programming but also achieves the desired high-quality optimal solutions in fewer iterations. After our experiment, we have optimized the various functions presented in this paper. [ABSTRACT FROM AUTHOR]

Projections onto sets are used in a wide variety of methods in optimization theory but not every method that uses projections really belongs to the class of projection methods as we mean it here. Here,projection methodsare iterative algorithms that use projections onto sets while relying on the general principle that when a family of (usually closed and convex) sets is present, then projections (or approximate projections) onto the given individual sets are easier to perform than projections onto other sets (intersections, image sets under some transformation, etc.) that are derived from the given family of individual sets. Projection methods employ projections (or approximate projections) onto convex sets in various ways. They may use different kinds of projections and, sometimes, even use different projections within the same algorithm. They serve to solve a variety of problems which are either of the feasibility or the optimization types. They have different algorithmic structures, of which some are particularly suitable for parallel computing, and they demonstrate nice convergence properties and/or good initial behavioural patterns. This class of algorithms has witnessed great progress in recent years and its member algorithms have been applied with success to many scientific, technological and mathematical problems. This annotated bibliography includes books and review papers on, or related to, projection methods that we know about, use and like. If you know of books or review papers that should be added to this list please contact us. [ABSTRACT FROM AUTHOR]

This note concerns the paper [Janiak A, Kovalyov MY, Lichtenstein M. On a single machine-scheduling problem with separated position and resource effects. Optimization; 2013. doi:10.1080/02331934.2013.804077], which presents an analysis, a counterexample and a pseudocode related with our proof of optimality for a resource allocation algorithm given in [Rudek A, Rudek R. A note on optimization in deteriorating systems using scheduling problems with the aging effect and resource allocation models. Comput. Math. Appl. 2011;62:1870–1878]. We show that the discussed analysis is based only on one part of our proof omitting its integral second part, which is the source of misunderstanding. The considered counterexample is applied for an algorithm, which was not the method presented in our paper, whereas our algorithm provides the correct result for the mentioned counterexample. The provided pseudocode of the resource allocation algorithm, which is presented as the correct method, is a pseudocode of the algorithm described in our paper. Therefore, we show that the results presented in our paper are correct. [ABSTRACT FROM AUTHOR]

The domain of material science has made great strides in recent years, especially in the fields of metallurgy and ceramic materials and the production of highly trustworthy, cost-effective and economically useful components for use in many industries. For the production of contours and intricate forms in conductive materials, Electrical Discharge Machining (EDM) is by far the most versatile and cost-effective alternative to standard machining processes. In-depth discussion and analysis of the following topics may be found throughout this review paper study. Surface topography and machining properties are investigated in this literature review to determine the impact of mechanical, chemical, electrochemical, and thermal material removal techniques. Different characteristics of dielectric mediums are also covered. Non-conductive and conductive ceramic composite performance characteristics, surfacetexture, mechanical and electrical qualities and geometrical tolerances are investigated as a function of electrode material. Through the perspective of composite materials, a variety of electrical discharge devices' performance metrics and properties are compared and contrasted. The performance metrics and characteristics of a wide variety of electrical discharge machines are compared and contrasted in light of the composite materials in order to identify their distinguishing characteristics. EDM research is being conducted on a variety of sophisticated conductive materials, to explore their unpredictable effects on EDM and their specialized applications. Methods for optimizing the study of composite material and their effects on EDM's numerous aspects include Design of Experiments, Analysis of Variance (ANOVA), Response Surface Methodology, Taguchi with Grey Relational Analysis, and so on. X-ray Diffraction (XRD) and Energy-Dispersive X-ray analysis (EDAX) are used to study the topography of a variety of composite materials and the latest optimization strategies are also investigated using a variety of algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes two novel approaches for the problem of energy management in hybrid electric vehicles. Shuffled frog-leaping algorithm (SFLA) is a recently proposed population-based optimization algorithm. This paper first formulates energy management as an optimization problem and optimizes the problem using SFLA. Then the paper makes use of SFLA as a training algorithm to train artificial neural network (ANN) and this SFLA-trained ANN is used for energy management. Interestingly, the proposed approaches of this paper are found to be robust and more efficient than contemporary approaches. [ABSTRACT FROM PUBLISHER]

In this paper, two modified spectral conjugate gradient methods which satisfy sufficient descent property are developed for unconstrained optimization problems. For uniformly convex problems, the first modified spectral type of conjugate gradient algorithm is proposed under the Wolfe line search rule. Moreover, the search direction of the modified spectral conjugate gradient method is sufficiently descent for uniformly convex functions. Furthermore, according to the Dai-Liao's conjugate condition, the second spectral type of conjugate gradient algorithm can generate some sufficient decent direction at each iteration for general functions. Therefore, the second method could be considered as a modification version of the Dai-Liao's algorithm. Under the suitable conditions, the proposed algorithms are globally convergent for uniformly convex functions and general functions. The numerical results show that the approaches presented in this paper are feasible and efficient. [ABSTRACT FROM AUTHOR]

Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join (kNNJQ) or closest pairs queries (kCPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in Apache Sedona, using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed kNNJQ and kCPQ distributed algorithms are efficient, scalable, and robust in Apache Sedona. Finally, Sedona is also compared to other similar cluster computing systems, showing the best performance for kCPQ and competitive results for kNNJQ. [ABSTRACT FROM AUTHOR]

A sparse array synthesis method based on improved wind-driven optimization algorithm (IWDO) and fast Fourier transform (FFT) for the purpose of sidelobe suppression was introduced. First, the uniform linear array was sparse by the FFT algorithm. Then the amplitude excitation weighting of array elements was optimized by IWDO algorithm based on probability factor chaotic map. During the optimization process, the minimum peak sidelobe level was used as the fitness function. By comparing with the simulation results of classic Chebyshev weighting method, the method proposed in this paper has better performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose, a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area, we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all, a continuous optimisation algorithm is used to solve a combinatorial optimisation problem where some efficient methods of converting a continuous vector to a discrete job permutation and vice versa are presented. These methods are not problem specific and can be employed in any continuous algorithm to tackle the permutation type of optimisation problems. Secondly, a variable parameter search is introduced for the differential evolution algorithm which significantly accelerates the search process for global optimisation and enhances the solution quality. Thirdly, some novel ways of calculating the total tardiness from makespan are introduced for the NIPFS problem. The performance of vpsDE is evaluated against a well-known RKGA from the literature. The computational results show its highly competitive performance when compared to RKGA. It is shown in this paper that the vpsDE performs better than the RKGA, thus providing an alternative solution approach to the literature that the RKGA can be well suited. [ABSTRACT FROM AUTHOR]

Proper fixture design is crucial to workpiece quality assurance in manufacturing. Incorrect fixture design may lead to workpiece deformation during machining. The fixture configuration design is one of the important aspects of fixture design. This paper deals with fixture layout optimization problem. The objective is to minimize the norm of all the passive contact forces satisfying Coulomb friction constraint, work-piece static equilibrium constraint and contact constraint, for the entire cutting operation. To solve this problem, the paper proposes Genetic Algorithm with Learning Automata (GALA) algorithm, which is a population based interconnected learning automata algorithm incorporating genetic operators. The algorithm enjoys the good characteristics of both GA and LA. It is validated with an example of face milling operation. The optimal layout is found to be in tune with empirical facts. Also, for the further investigation of the algorithm, it has been tested on a different problem sets and a comparative study is carried out. [ABSTRACT FROM AUTHOR]

In this paper, the k-median of a graph is used to decompose the domain (mesh) of the continuous two- and three-dimensional finite element models. The problem of k-median is stated as an optimization problem and is solved by utilizing eight robust meta-heuristic algorithms. The Artificial Bee Colony algorithm (ABC), Cyclical Parthenogenesis algorithm (CPA), Cuckoo Search algorithm (CS), Teaching-Learning Based Optimization algorithm (TLBO), Tug of War Optimization algorithm (TWO), Water Evaporation Optimization algorithm (WEO), Ray Optimization algorithm (RO), and Vibrating Particles System algorithm (VPS) constitute the set of algorithms that are employed in the present study. In order to tune the parameters of the meta-heuristics, the Taguchi method is used. The efficiency and robustness of the algorithms are investigated through two- and three- dimensional finite element models. [ABSTRACT FROM AUTHOR]

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at . [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed smooth optimisation problem of multi-agent systems over undirected connected communication topologies. The objective is to minimise the global objective function, which is the sum of local private objective functions, by exchanging and computing local information among each agent or with its neighbourhoods. To avoid continuous communication among agents and reducing communication overheads, we propose an event-triggered distributed optimisation algorithm based on a proportional-integral control strategy. We first show that the developed algorithm does not exhibit Zeno behaviour. Then, it is shown that the proposed algorithm exponentially converges to an exact global minimiser under the restricted secant inequality condition. This condition admits a more general class of the global objective function since it does not require the convexity of the global objective function and the global minimisers are not required to be unique. Theoretical results are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, the problem of motion planning for parallel robots in the presence of static and dynamic obstacles has been investigated. The proposed algorithm can be regarded as a synergy of convex optimization with discrete optimization and receding horizon. This algorithm has several advantages, including absence of trapping in local optimums and a high computational speed. This problem has been fully analyzed for two three-DOF parallel robots, ie 3s-RPR parallel mechanism and the so-called Tripteron, while the shortest path is selected as the objective function. It should be noted that the first case study is a parallel mechanism with complex singularity loci expression from a convex optimization problem standpoint, while the second case is a parallel manipulator for which each limb has two links, an issue which increases the complexity of the optimization problem. Since some of the constraints are non-convex, two approaches are introduced in order to convexify them: (1) A McCormick-based relaxation merged with a branch-and-prune algorithm to prevent it from becoming too loose and (2) a first-order approximation which linearizes the non-convex quadratic constraints. The computational time for the approaches presented in this paper is considerably low, which will pave the way for online applications. [ABSTRACT FROM PUBLISHER]

In order to solve the problem of distribution network reconstruction with distributed generation (DG) and electric vehicle (EV), a multi-objective distribution network reconstruction model with DG and EV is established in this study. Two rules for opening the loop are proposed to reduce the probability of infeasible solutions. Some measures are proposed to improve traditional gravitational algorithm (GSA). Firstly, the particle swarm algorithm (PSO) is combined to improves the update formula of speed and position. In this way, the global search capability of the GSA is enhanced, which gives the best performance with respect to jump out of the local traps. Furthermore, the processing method for agents that cross the boundary is improved, which increases the diversity of samples while generating elite particles. Hence, this method can improve the efficiency of the algorithm. Finally, the variability of load, DG and EV is considered for dynamic reconstruction. The validity of the optimization algorithm and refactoring strategy are demonstrated by case studies in the paper. [ABSTRACT FROM AUTHOR]

This paper introduces an improved particle swarm optimisation algorithm (IPSO), to select satellites rapidly in multi-GNSS marine positioning. The traditional particle swarm optimisation (PSO) may be trapped into local optimisation. To avoid the disadvantage, the proposed algorithm uses linear inertia weight factor and two functions of the immune system, i.e. the memory function and the self-regulatory function. Several experiments are carried out by adopting real survey data collected by the SiNan receiver that is installed on the Snow Dragon scientific research ship during the 9th China Arctic expedition. Compared with the minimum Geometric dilution of precision (GDOP) method, PSO and IPSO significantly reduce the computing time (96.25% and 95.61%). The variance of IPSO is 0.063, which is much lower than that of PSO (0.087). As for the positioning accuracy, the IPSO can reach the centimetre level in the kinematics condition. [ABSTRACT FROM AUTHOR]

Confronting the challenges provided by jumbo container vessels, managers of world-class seaports must take into consideration water depths-related constraints when making the vessel berthing decisions. That is, when the water depths satisfy the requirement of jumbo vessels' drafts, these vessels are allowed to sail through the navigation channel. In this paper, we study a tactical berth allocation problem under the consideration of time-varying water depths. For the resolution, we propose an integer programming model, a set partitioning formulation and an improved differential evolutionary algorithm. Computational experiments demonstrate that the set partitioning formulation outperforms the integer programming model and can be applied to solve small- and medium-sized problems. The improved differential evolutionary algorithm is recommended for large-sized problems. [ABSTRACT FROM AUTHOR]

Green treatment on Waste Electrical and Electronic Equipmenthas increasingly attracted attention due to its significant environmental benefits and potential recovery earnings. Automated disassembly has been regarded as a powerful solution to enable more efficient recovery operations. Although numerous studies have contributed to the issues of disassembly, there are few researches that focus on decision model for selecting disassembly system scheme and recovery route in automated disassembly. In this paper, we propose a two-phase joint decision-making model to address this problem with the goal of balancing disassembly profit with environmental impact. First, we establish a multi-objective optimisation model to obtain the Pareto optimal recovery routes for each automated disassembly system scheme. Both recovery profit and energy consumption are evaluated for multi-station disassembly system. We design a multi-objective hybrid particle swarm optimisation algorithm based on symbiotic evolutionary mechanism to solve the proposed model. Then, we compare the Pareto optimal solutions of all the system schemes using a fuzzy set method and identify the best scheme. Finally, we conduct real case studies on the automated disassembly of different waste electric metres. The results demonstrate the superiority of automated disassembly and validate the effectiveness of our proposed model and algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid (MFO-SCA) method based on Moth Flame Optimization (MFO) algorithm and Sine Cosine Algorithm (SCA). MFO is utilized for the exploitation phase while SCA is used for the exploration phase. In addition, the hybrid MFO-SCA method is used with combined power loss sensitivity (CPLS) to determine the optimal allocation of single and multiple of distributed generations (DGs) and capacitors in distribution system. In this paper, the total active power loss is used as a single objective function for incorporating DGs and capacitors in RDS. Also, this paper studies the system performance when integrating DGs or capacitors alone and DGs with capacitors simultaneously in RDS. The proposed method is validated using the standard IEEE 69-bus and IEEE 33-bus radial distribution system (RDS). From comparative study, the novel hybrid MFO-SCA proved its effectiveness in obtaining the best global optimum results of the objective function compared with other recent optimization techniques, especially SCA and MFO algorithms. [ABSTRACT FROM AUTHOR]

A novel Lyapunov-based methodology for both stability analysis and stabilisation of second-order sliding mode algorithms in the presence of exogenous disturbances is presented. Nowadays, second-order sliding modes such as the well-known super-twisting algorithm are widely recognised for their usefulness in the design of controllers, observers, and exact differentiators. However, a theoretical proof for an optimal and systematical selection of parameters for the algorithm to be stable is of particular interest. The solution proposed in this paper is based on combining exact convex representations of the nonlinear algorithms with a strict Lyapunov function, which leads to stability or design conditions in terms of linear matrix inequalities that can be efficiently solved via convex optimisation techniques, allowing us to provide an enlarged and optimal parameter setting in contrast with existing results; moreover, the systematic nature of the proposal is able to deal with different scenarios avoiding ad-hoc solutions of previous works. Effectiveness of the results is verified via simulation examples. [ABSTRACT FROM AUTHOR]

Min–max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex–concave min–max problem is an active topic of research with efficient algorithms and sound theoretical foundations developed. However, it remains a challenge to design provably efficient algorithms for non-convex min–max problems with or without smoothness. In this paper, we study a family of non-convex min–max problems, whose objective function is weakly convex in the variables of minimization and is concave in the variables of maximization. We propose a proximally guided stochastic subgradient method and a proximally guided stochastic variance-reduced method for the non-smooth and smooth instances, respectively, in this family of problems. We analyse the time complexities of the proposed methods for finding a nearly stationary point of the outer minimization problem corresponding to the min–max problem. [ABSTRACT FROM AUTHOR]

Estimating ego motion of monocular visual system from the input image sequence is a critical problem in computer vision. This paper considers this problem from the view of time domain optimisation. At first, based on the frame to frame matches, the traditional monocular visual navigation algorithm is introduced, and this process is also seen as a two-view method. On the basis of the three-view matches, the trifocal tensor based monocular navigation algorithm is described. In addition, this paper proposes a novel moving horizon estimation (MHE) based algorithm to tackle the pose estimation problem. In this algorithm, the epipolar constraints and the closed loop constraints of position along with consecutive time steps are all involved in the global optimisation model. In accordance with this optimisation function, the MHE is introduced to achieve the tradeoff between computation costs and estimation accuracy. Because of the inherent robustness of the referred moving horizon estimator, it is becoming more popular in various applications. Based on the general epipolar constraints, trifocal tensor constraints and step crossed optimisation function during the whole moving window, the corresponding three referred pose estimation algorithms are all implemented comparatively at the end of this paper. The rotation and translation experiments are implemented to validate the improvements of the time domain optimisation methods. [ABSTRACT FROM AUTHOR]

The objective of this paper is to develop intelligent search heuristics to solve n-jobs, m-machines lot streaming problem in a flow shop with equal size sub-lots where the objective is to minimise makespan and total flow time independently. Improved sheep flock heredity algorithm (ISFHA) and artificial bee colony (ABC) algorithms are applied to the problem above mentioned. The performance of these algorithms is evaluated against the algorithms reported in the literature. The computational analysis shows the better performance of ISFHA and ABC algorithms. [ABSTRACT FROM AUTHOR]

In this paper, work concerning robust D-stability analysis is considered for discrete descriptor systems with structured uncertainties and multiple time delays. A genetic algorithm is used to make the stability test of the main result more efficient. A delay-dependent robust D-stability criterion is proposed to ensure that all poles of the discrete multiple time-delay descriptor systems subject to uncertainties are located within a disk contained in the unit circle. It will be seen that the system considered in this paper is more general and the proposed robust D-stability criterion is less conservative and easier to check than those presented in recent research. Moreover, a delay-dependent criterion is derived to guarantee that the system is regular, impulse-free, and D-stable. Since the boundary test occurs during the check of the criteria, a genetic algorithm is used to make it more precise. Finally, an example is introduced to illustrate our main results. [ABSTRACT FROM AUTHOR]

The traditional service restoration methods are experiencing a significant challenge because the performance of such methods is often dominated by very large processing time. Therefore, it is important to develop a fast and an accurate method to solve a complex non-linear optimization problem representing the service restoration problem in a distribution system. In this paper, due to a binary nature of the service restoration problem, the Binary Water Cycle Algorithm (BWCA) is proposed to reduce out-of-service loads and system losses considering operational constraints, the radial topology of the distribution systems, and the influence of the presence of distributed generations (DGs) on the service restoration problem. The proposed method is a modified form of the Water Cycle Algorithm (WCA) that is mainly used for discrete and continuous optimization problems. The effectiveness of the proposed method is validated by comparing with other well-known meta-heuristic algorithms using a modified IEEE 33-node radial distribution system. The simulation results demonstrate the robustness of this method to provide a precise decision with short convergence time for solving service restoration problems. [ABSTRACT FROM AUTHOR]

In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches. [ABSTRACT FROM AUTHOR]

Job-shop scheduling is a typical NP-hard problem which has drawn continuous attention from researchers. In this paper, the Intelligent Water Drops (IWD) algorithm, which is a new meta-heuristics, is customised for solving job-shop scheduling problems. Five schemes are proposed to improve the original IWD algorithm, and the improved algorithm is named the Enhanced IWD algorithm (EIWD) algorithm. The optimisation objective is the makespan of the schedule. Experimental results show that the EIWD algorithm is able to find better solutions for the standard benchmark instances than the existing algorithms. This paper has made a contribution in two aspects. First, to the best of the authors’ knowledge, this research is the first to apply the IWD algorithm to the job-shop scheduling problem. This work can inspire further studies of applying IWD algorithm to other scheduling problems, such as open-shop scheduling and flow-shop scheduling. Second, this research further improves the original IWD algorithm by employing five schemes to increase the diversity of the solution space as well as the solution quality. [ABSTRACT FROM PUBLISHER]

In this paper, multi-objective flow-shop scheduling with parallel machines is considered, and an algorithm for production-scheduling optimisation is proposed. The objective is to design an algorithm to optimise production scheduling, taking into consideration customer changes to delivery dates and quantities, materials requisitioned from the supplier and inventory management. Manufacturing lead time for the production order is minimised, and, finally, resource use is considered as an auxiliary criterion. Numerical examples, modelled with data from a Mexican enterprise, and computational results are reported. The paper indicates that the mathematical model, as well as the program that solves it, provides flexibility with minimum modifications. Thus it can provide useful information for multiple decision processes. [ABSTRACT FROM AUTHOR]

Assembly line balancing problems basically consist of assigning a set of tasks to a group of workstations while maintaining the tasks' precedence relations, which are represented by a predetermined precedence graph. However, one or more parts of a product's assembly process may admit alternative precedence subgraphs, which represent possible assembly variants. In general, because of the great difficulty of the problem and the impossibility of representing alternative subgraphs in a precedence graph, the system designer will decide to select, a priori, one of such alternative subgraphs. This paper presents, characterizes and formulates a new general assembly line balancing problem with practical relevance: the alternative subgraphs assembly line balancing problem (ASALBP). Its novel characteristic is that it considers the possibility of having alternative assembly subgraphs, with the processing times and/or the precedence relations of certain tasks dependent on the assembly subgraph selected. Therefore, solving this problem implies simultaneously selecting an assembly subgraph for each part of the assembly that allows alternatives and balancing the line. The potentially positive effects of this on the solution of the problem are shown in a numerical example. Finally, a simple mathematical programming model is described and the results of a brief computational experiment are presented. [ABSTRACT FROM AUTHOR]

Recent dismal economic conditions and a ruthlessly competitive environment have forced many companies to restructure, or reorganize their priorities. For such companies, the concentration of various resources upon their particular corporate strong points has become a central strategy. Consequently, there has been a rapid increase in the importance of (1) selecting profitable projects from a wealth of possible alternatives and (2) optimizing the allocation of current resources among the selected projects. This paper proposes an optimization system for project selection that not only yields the most beneficial project set, but also the optimum allocation of human resources for the selected projects. The optimization system consists of two algorithms, namely (1) a project selection algorithm for choosing the set of projects that maximizes the total estimated profit, and (2) a human resource allocation algorithm for optimally placing human resources among the selected projects, having considered the satisfaction level provided by each employee's skills, personal motivation and career goals. [ABSTRACT FROM AUTHOR]

This paper presents ANTBAL, an ant colony optimization algorithm for balancing mixed-model assembly lines. The proposed algorithm accounts for zoning constraints and parallel workstations and aims to minimize the number of operators in the assembly line for a given cycle time. In addition to this goal, ANTBAL looks for solutions that smooth the workload among workstations, which is an important aspect to account for in balancing mixed-model assembly lines. Computational experience shows the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents tight mathematical programming formulations for the dynamic demand joint replenishment problem (DJRP). Experimental studies using general-purpose software document the improved computational efficiency of the new formulations versus the earlier models of Boctor et al. (Boctor, F.F., Laporte, G. and Renaud, J., Models and algorithms for the dynamic-demand joint replenishment problem. Int. J. Prod. Res. , 2004, 42, 2667–2678.) and Robinson and Gao (Robinson, E.P. and Gao, L., A dual-ascent procedure for multi-product dynamic demand coordinated replenishment with backlogging. Manag. Sci. , 1996, 42, 1556–1564.). The findings encourage the development of specialized algorithms for their solution. The paper also evaluates the performance of the perturbation and dual-ascent heuristics for solving DJRP finding the perturbation heuristic is superior at relatively low set-up cost ratios and high joint set-up cost levels, while the dual-ascent heuristic strongly dominates at relatively high set-up cost ratios and low joint set-up costs. Considering that both heuristics are computationally efficient, the best application strategy is to solve the problem with both heuristics and implement the best found solution. [ABSTRACT FROM AUTHOR]

In order to obtain the initial condition of the traffic accident and reconstruct the collision process of motorcycle and car in reverse, this paper proposes a multi-objective optimisation algorithm based on the combination of NSGA-II and surrogate model. Aiming at a collision accident between a car and motorcycle, a simulation model of the human-vehicle-road coupling system is established. First, select the optimisation variables, optimisation objectives and constraints, define the variable range, and through the Opt-LHD to sample the optimisation variables. Then, obtain and fit the sample data, and take the sample data as the initial conditions into the established motorcycle traffic accident simulation model. Next, obtain the simulation data of the optimisation variables, and establish the response surface model of the optimisation objectives in MATLAB. Finally, using NSGA-II algorithm, the individuals in the population are solved based on the response surface model in turn, the Pareto solution set is obtained, and a group of satisfactory solutions is taken back to the simulation model for verification of the optimisation model, so as to prove the correctness of the satisfactory solution, and then the initial conditions of the collision between the motorcycle and the car are obtained. [ABSTRACT FROM AUTHOR]

An improved particle swarm optimization (PSO) with adaptive weighted delay velocity (PSO-AWDV) is proposed in this paper. A new scheme blending weighted delay velocity is firstly presented for a new PSO with weighted delay velocity (PSO-WDV) algorithm. Then, to adaptively update the velocity inertia weight, an adaptive PSO-AWDV algorithm is developed based on the evolutionary state of the particle swarm evaluated via a new estimation method. The newly proposed adaptive PSO-AWDV algorithm is tested based on some famous benchmark functions, which can confirm that the performance of PSO-AWDV is superior to several well-known PSO variants and intelligent optimization algorithms in literature. [ABSTRACT FROM AUTHOR]

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work. [ABSTRACT FROM AUTHOR]

In this paper we present a framework to analyze the asymptotic behavior of two timescale stochastic approximation algorithms including those with set-valued mean fields. This paper builds on the works of Borkar and Perkins & Leslie. The framework presented herein is more general as compared to the synchronous two timescale framework of Perkins & Leslie, however the assumptions involved are easily verifiable. As an application, we use this framework to analyze the two timescale stochastic approximation algorithm corresponding to the Lagrangian dual problem in optimization theory. [ABSTRACT FROM PUBLISHER]

The optimal allocation of distributed manufacturing resources is a challenging task for supply chain deployment in the current competitive and dynamic manufacturing environments, and is characterised by multiple objectives including time, cost, quality and risk that require simultaneous considerations. This paper presents an improved variant of the Teaching-Learning-Based Optimisation (TLBO) algorithm to concurrently evaluate, select and sequence the candidate distributed manufacturing resources allocated to subtasks comprising the supply chain, while dealing with the trade-offs among multiple objectives. Several algorithm-specific improvements are suggested to extend the standard form of TLBO algorithm, which is only well suited for the one-dimensional continuous numerical optimisation problem well, to solve the two-dimensional (i.e. both resource selection and resource sequencing) discrete combinatorial optimisation problem for concurrent allocation of distributed manufacturing resources through a focused trade-off within the constrained set of Pareto optimal solutions. The experimental simulation results showed that the proposed approach can obtain a better manufacturing resource allocation plan than the current standard meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimisation and Harmony Search. Moreover, a near optimal resource allocation plan can be obtained with linear algorithmic complexity as the problem scale increases greatly. [ABSTRACT FROM PUBLISHER]

This paper focuses on simultaneous optimisation of production planning and scheduling problem over a time period for synchronous assembly lines. Differing from traditional top-down approaches, a mixed integer programming model which jointly considers production planning and detailed scheduling constraints is formulated, and a Lagrangian relaxation method is developed for the proposed model, whereby the integrated problem is decomposed into planning, batch sequencing, tardiness and earliness sub-problems. The scheduling sub-problem is modelled as a time-dependent travelling salesman problem, which is solved using a dynasearch algorithm. A proposition of Lagrangian multipliers is established to accelerate the convergence speed of the proposed algorithm. The average direction strategy is employed to solve the Lagrangian dual problem. Test results demonstrate that the proposed model and algorithm are effective and efficient. [ABSTRACT FROM PUBLISHER]

In this paper, pixelated Frequency Selective Surface (FSS) layer embedded in two carbonyl iron-filled silicon rubber (CISR) sheets are used for design of broadband microwave absorber in frequency range of 3.95 to 8.2 GHz. Top and bottom layers of CISR sheets are prepared with 24% and 33% of Carbonyl Iron (CI) powder by volume, respectively. Reflectivity of microwave absorber, complex permittivity and complex permeability of CISR sheets are evaluated using two Rectangular Dielectric Waveguide (RDWG) measurement systems in 3.95 to 5.85 GHz frequency band (WR-187) and 5.85 to 8.2 GHz frequency band (WR-137). An algorithm was developed using Geometry-Refinement Method (GRM) and Genetic Algorithm (GA) optimization technique to design pixelated FSS layer. Measured as well as simulated reflectivity of composite microwave absorber is better than −10 dB at normal incidence in the frequency range of 3.95–8.2 GHz. For oblique incidence from 0 ∘ to 40 ∘ , simulated reflectivity of TE and TM polarizations carried out for all angles of incidence. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm, known as Refraction based Whale Optimization Algorithm (RWOA), for providing optimal solutions to the Combined Economic Emission Dispatch problem based on the production of an optimum solar energy system. It validates the robustness of the proposed algorithm in Hybrid Renewable Energy Systems (HRES). To control the emission and economic cost, it is necessary to connect the wind turbine with the thermal power plant. Finally, the performance of the proposed approach is compared with the other conventional approaches concerning the economic cost, emission cost transmission loss. Accordingly, the results, shows that the proposed RWOA method is 92.81%, 99.2%, 92.8%, 80%, 37.8%, 47.34%, and 45.83% superior to GA, GWO, WOA, DA, GWSO, LGSO and WWO at 40th iteration. [ABSTRACT FROM AUTHOR]

At present, with the emergence and development of cloud manufacturing (CMfg), the scale of services in CMfg platforms increases rapidly which provide the same or familiar functionality but different performance. Large-scale cloud service composition and optimization (CSCO) problems is one of the key issues for the implementation of CMfg. To deal with this NP-hard problem, a novel hybrid algorithm called Bee-Colony Simplex method hybrid Algorithm (ABCSA) for CSCO problems is proposed in this paper, which employs both the Simplex method and chaotic and global best guided strategy. The random-evolve Simplex method is proposed to maintain the algorithm work efficiently to keep the population diversity and avoid premature convergence. The global best guided and chaos searching strategy is proposed to avoid local optimization. To evaluate the effectiveness and efficiency, simulation and analysis of the experiments are carried out, and the results clearly prove the superior performance of ABCSA over existing intelligent optimization algorithms in the CSCO problems. [ABSTRACT FROM AUTHOR]

In this paper, we present a feasible predictor-corrector interior-point method for symmetric cone optimization problem in the large neighbourhood of the central path. The method is generalization of Ai-Zhang's predictor-corrector algorithm to the symmetric cone optimization problem. Starting with a feasible point in given large neighbourhood of the central path, the algorithm still terminates in at most iterations. This matches the best known iteration bound that is usually achieved by short-step methods, thereby, closing the complexity gap between long- and short-step interior-point methods for symmetric cone optimization. The preliminary numerical results on a selected set of NETLIB problems show advantage of the method in comparison with the version of the algorithm that is not based on the predictor-corrector scheme. [ABSTRACT FROM AUTHOR]

This paper establishes a theoretical framework of infeasible Mehrotra-type predictor-corrector algorithm for Cartesian nonlinear complementarity problems over symmetric cones which can be regarded as an extension of Liu et al. [Polynomial convergence of Mehrotra-type predictor-corrector algorithm for the Cartesian -LCP over symmetric cones, Optimization 64(4) (2013), pp. 1-23] for Cartesian linear complementarity problems over symmetric cones to Cartesian nonlinear complementarity problems over symmetric cones. The iteration complexities of the proposed algorithm are estimated and some numerical results are provided. The numerical results show that the proposed algorithm is efficient and reliable. [ABSTRACT FROM AUTHOR]