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Internet of Things (IoT) has the characteristics of multi-source, multi-type and unequal amount of task. Considering multiple requests at stochastic moment in the agricultural IoT service, an optimization model of minimum service cost is built in this paper, and an adaptive immune algorithm with endocrine regulation (AIE) is proposed to solve the problem of dynamic service for multiple requests. Based on this model, two service providing strategies are put forward, i.e. single service and collaborative service. The former means that one request task is completed by one service provider, while the latter means that one request task is collaboratively completed by multiple service providers. The service cost and service time are respectively evaluated under two service providing strategies. The simulation results verify the feasibility and effectiveness of the proposed AIE through comparison to the immune algorithm without endocrine regulation. Meanwhile, it is demonstrated that the proposed AIE can obtain more excellent performance than genetic algorithm and particle swarm optimization. [ABSTRACT FROM AUTHOR]

This paper applies a new hybrid method by a combination of three population base algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). The proposed method has been inspired from natural selection process and it completes this process in GA by using the PSO and SOS. It tends to minimize the execution time and in addition to reduce the complexity. Symbiotic organisms search is a robust and powerful metaheuristic algorithm which has attracted increasing attention in recent decades. There are three alternative phases in the proposed algorithm: GA, which develops and selects best population for the next phases, PSO, which gets experiences for each appropriate solution and updates them as well and SOS, which benefits from previous phases and performs symbiotic interaction update phases in the real-world population. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. It has further been evaluated in, the experiment on the clustering of benchmark datasets. The obtained results from basic and non-parametric statistical tests confirmed that this hybrid method dominates in terms of convergence, execution time, success rate. It optimizes the high dimensional and complex functions Rosenbrock and Griewank up to 10 − 330 accuracy in less than 3 s, out-performing other known algorithms. It had also applied clustering datasets with minimum intra-cluster distance and error rate. [ABSTRACT FROM AUTHOR]

Optimal facility placement is key in field development planning. However, this process should be integrated with well placement and well trajectory design which poses a real challenge especially early in the field development planning; at the concept screening phase. During that phase, multiple concepts need to be assessed within, typically, a tight project time line. The integrated workflow becomes prohibitively expensive and calls, hence, for a fast and robust method that fulfils the objectives in the context of real onshore and offshore field development planning. Facility placement optimization consists mainly of minimizing the resulting cost while honouring topological complexities and prescribed capacity and trajectory constraints. It aims at selecting the optimal number and location of the different "nodes" comprising the facility and the optimal paths of the connections (pipelines) between nodes. Although many optimization schemes are developed and widely available in the literature, these methods are prohibitively slow with exhaustive search required, or they do not account for the various topological complexities typically encountered in real scenarios. Thus, the deficiency associated with these optimization schemes are drastically limiting in the case of the concept-select phase of field development planning where many scenarios need to be considered; in a relatively short timeframe. Deterministic optimization methods hit memory and computational limitations for all but trivial scenarios and, hence, fail to address the problem. Recently, genetic algorithms were applied with promising results addressing this challenge. In this paper, particle swarm optimization (PSO) is used for facility placement optimization and coupled with the A* algorithm for pipelines layout. This paper illustrates the capability of the implemented modular PSO-based approach for facility placement optimization through various levels of problem complexity: multiple facility layers, topological complexity and prohibited areas. The proposed algorithms are apt to be used in real field development planning projects and are, to be best of our knowledge, the first published material addressing the problem with the required level of speed, robustness and integration. • Modular approach for facility placement optimization using PSO coupled with A* algorithm for pipelines layout optimization. • The algorithm addresses various levels of problem complexity: multiple layers, topological complexity and prohibited areas. • The algorithm is apt to be used in real field development planning with the required speed, robustness and integration. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a self-adjusting e-course generation process, which support to provide a truly personalized learning environment. The proposed process is divided into four steps: (1) determining learning concept structure, (2) adjusting the difficulty of the e-learning material, (3) analyzing a learner’s ability and learning goals, and (4) composing personalized e-courses. Meanwhile, this paper applies the collaborative voting approach to determine the difficulty of the e-learning material, and the maximum likelihood estimation (MLE) to analyze a learner’s ability and her/his learning goals. Since evolutionary algorithms (EAs) have been developed to find close optimal solutions, this paper adopts them to compose personalized e-courses that meet individual learners’ demands. Once a learner learns one or more learning concepts covered in a personalized e-course, the feedback information from the learner must be returned to: (I) self-adjust the difficulty of the e-learning material for step 2, and (II) update the learners’ ability and learning goals for step 3. Furthermore, to find appropriate EAs for personalized e-course composition, this paper devises some experiments to compare two widely applied EAs, Genetic algorithms (GA) and Particle Swarm Optimization (PSO). When the number of e-learning materials is less than 300, the experimented results indicate that the executing effectiveness of PSO is better than that of GA. Besides, to validate the practicability of the proposed process, an e-course authoring tool based on the proposed process is developed to generate personalized e-courses. The generated personalized e-courses have been provided to 103 actual learners who participate in an “Introduction to Computer” curriculum. The investigation results indicate that the proposed process adapts to learners by utilizing the feedback from many learners. In other words, learning experiences of one organization/class can benefit to another organization/class’s learners in the same curriculum. [Copyright &y& Elsevier]

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design problems. This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building (nZEB) where more than 1.6 10 solutions would be possible. The compared algorithms include a controlled non-dominated sorting genetic algorithm with a passive archive (pNSGA-II), a multi-objective particle swarm optimization (MOPSO), a two-phase optimization using the genetic algorithm (PR_GA), an elitist non-dominated sorting evolution strategy (ENSES), a multi-objective evolutionary algorithm based on the concept of epsilon dominance (evMOGA), a multi-objective differential evolution algorithm (spMODE-II), and a multi-objective dragonfly algorithm (MODA). Several criteria was used to compare performance of these algorithms. In most cases, the quality of the obtained solutions was improved when the number of generations was increased. The optimization results of running each algorithm 20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity, followed by the pNSGA-II, evMOGA and spMODE-II. Uncompetitive results were achieved by the ENSES, MOPSO and MODA in most running cases. The study also found that 1400–1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. [ABSTRACT FROM AUTHOR]

• Mathematical modeling of the torsional-bar and gas-spring based trunk lid mechanisms for sedan type of vehicles. • An automated structural design approach for two different trunk lid mechanisms. • Minimizing the differences between desired and calculated driving forces. • Comparing the performance of population-based PSO, GA and DE optimization algorithms. This paper proposes a parametric synthesis of two different trunk lid mechanisms for sedan vehicles, incorporating three different population-based optimization techniques: Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and the Differential Evolution (DE). For this purpose, the kinematic equations of the mechanisms are derived and implemented in the quasi-static analysis to determine the necessary driving forces. The lengths and initial angles of four-bar linkage are optimized in order to minimize the sum of the differences between the target and calculated hand force values. The optimization results of the design variables and the performances of the optimization methods are presented. The outcomes indicate that the proposed design procedure is able to provide a trunk lid mechanism in which the target value of driving force in every moving angle is achieved. Furthermore, it is observed that the optimization techniques show different performances due to the fact that they provide better and faster optimal solutions than each other for different cases. The results of this paper are of utmost importance for the manufacturer to obtain an automated design process for the trunk lid mechanisms. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient optimal sizing strategy for hybrid electric air-ground vehicles. First, to optimize the cost and energy consumption while maintaining driving performance, major design parameters, such as the number of battery packs, power of the engine-generator set, and air/ground electrical motors, are chosen as optimization variables. Second, an adaptive spiral optimization algorithm is proposed in the presented optimal sizing strategy. Third, unique update and punish mechanisms are designed to prevent the sizing process from being stuck in local minima, leading to improved searching efficiency. The simulation is carried out in MATLAB/Simulink using the Bogacki-Shampine solver. And the relevant coding work is implemented in MATLAB. Optimization results show that, compared to the initial sizing, the proposed strategy improves cost, energy consumption, ground-driving, and flying performance by 15.16%, 4.68%, 11.78%, and 1.43%, respectively. Finally, the proposed algorithm outperforms the original spiral optimization algorithm, enhanced genetic algorithm, and adaptive particle swarm optimization algorithm in terms of improving the objective function by 1.9%, 1.18%, and 2.04%, respectively. Theoretical insights for a hybrid electric air-ground vehicle powertrain sizing problem might be provided by the proposed strategy. • An optimal sizing strategy is proposed for hybrid electric air-ground vehicles. • The sizing problem is solved using an adaptive spiral optimization algorithm. • Unique update and punish methods are proposed to avoid being stuck in local minima. • The proposed strategy is verified through the comparison among different algorithms. [ABSTRACT FROM AUTHOR]

With the complexity of multi-objective optimization scenarios, both convergence and diversity of optimization algorithms are put forward higher requirements, but the harmony between the two has not been completely resolved, especially in controller tuning issues. To address this challenge, a novel competitive mechanism-based multi-objective bat algorithm is proposed in this paper. Firstly, based on a pairwise competition strategy, a competitive bat algorithm is designed as a candidate evolution strategy that promotes the population to converge quickly without cumbersome external archives. Furthermore, to avoid premature convergence, the genetic algorithm is utilized to diversity the swarm. Secondly, a designed tribal competition mechanism achieves a dynamical adjusting of evolution strategies according to maturity. Thence, the proposed algorithm can effectively balance the convergence and diversity through the adaptive complementation of multiple evolution strategies. Finally, experiments on benchmark functions illustrate that the proposed algorithm statistically outperforms the compared 12 state-of-the-art algorithms on at least 15 out of 19 instances. Besides, the proposed algorithm is used to solve multi-objective tuning problems of two widely used controllers in a laboratory-developed permanent magnet synchronous motor system. Comparison with 4 representative algorithms verifies its effectiveness and practicality in real-life multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

• A general optimization algorithm for a cooperative hyper-heuristics is described. • Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization are included • A Master processor communicates with all agents and ranks solution candidates • Both on-line parameter retuning and parallel processing speed up the search. • It was effective to optimise PSE models within acceptable computational times. This paper presents the general framework of a parallel cooperative hyper-heuristic optimizer (PCHO) to solve systems of nonlinear algebraic equations with equality and inequality constraints. The algorithm comprises the classical metaheuristics called Genetic Algorithms, Simulated Annealing and Particle Swarm Optimization, whose parameters are adaptively chosen during the executions. A Master-Worker architecture was designed and implemented, where the Master processor ranks the solution candidates informed by the metaheuristics and immediately communicates the most promising candidate to update all Workers. Algorithmic performance was tested with general models, most of them corresponding to PSE process systems. The results confirmed the efficiency of the proposed approach since both online parameter retuning and parallel processing sped up the search. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Detailed R-C-L-M models of power transformers, which are based on lumped parameters, are used extensively not only for transient analysis of power transformers to determine electrical stresses in windings, but also for studying transients in power systems. Models with few elements are generally more practicable for power system studies but at the expense of accuracy. The use of artificial methods to reduce an R-C-L-M model is the main contribution of this paper. Advantages of the suggested method include: (1) a reduced loss of accuracy compared with the original model and (2) the flexibility to choose the number of model elements to achieve the desired model depending on size and accuracy. The ability of three different artificial methods, genetic algorithm, particle swarm optimization, and bacterial foraging algorithm, to model reduction is evaluated using measurements on an actual 400kV test object and the results are compared with those obtained by common analytical formulae. [Copyright &y& Elsevier]

Abstract: In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency. [Copyright &y& Elsevier]

Abstract: This paper presents new multi-objective evolutionary hybrid algorithms for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithms are memetic Pareto particle swarm optimization based RBFN (MPPSON), Memetic Elitist Pareto non dominated sorting genetic algorithm based RBFN (MEPGAN) and Memetic Elitist Pareto non dominated sorting differential evolution based RBFN (MEPDEN). The proposed methods integrate accuracy and structure of RBFN simultaneously. These algorithms are implemented on two-class and multiclass pattern classification problems with one complex real problem. The results reveal that the proposed methods are viable, and provide an effective means to solve multi-objective RBFNs with good generalization ability and simple network structure. The accuracy and complexity of the network obtained by the proposed algorithms are compared through statistical tests. This study shows that the proposed methods obtain RBFNs with an appropriate balance between accuracy and simplicity. [Copyright &y& Elsevier]

Abstract: Multilevel thresholding is one of the most popular image segmentation techniques. In order to determine the thresholds, most methods use the histogram of the image. This paper proposes multilevel thresholding for histogram-based image segmentation using modified bacterial foraging (MBF) algorithm. To improve the global searching ability and convergence speed of the bacterial foraging algorithm, the best bacteria among all the chemotactic steps are passed to the subsequent generations. The optimal thresholds are found by maximizing Kapur''s (entropy criterion) and Otsu''s (between-class variance) thresholding functions using MBF algorithm. The superiority of the proposed algorithm is demonstrated by considering fourteen benchmark images and compared with other existing approaches namely bacterial foraging (BF) algorithm, particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The findings affirmed the robustness, fast convergence and proficiency of the proposed MBF over other existing techniques. Experimental results show that the Otsu based optimization method converges quickly as compared with Kapur''s method. [Copyright &y& Elsevier]

Abstract: This study employs differential evolution algorithm to solve the optimal chiller loading problem for reducing energy consumption. To testify the performance of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with those of the Lagrangian method, genetic algorithm and particle swarm algorithm. The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions. Moreover, it outperforms the genetic algorithm in finding optimal solution and also overcomes the divergence problem caused by the Lagrangian method occurring at low demands. [ABSTRACT FROM AUTHOR]

Abstract: QoS multicast routing in networks is a very important research issue in networks and distributed systems. It is also a challenging and hard problem for high-performance networks of the next generation. Due to its NP-completeness, many heuristic methods have been employed to solve the problem. This paper proposes the modified quantum-behaved particle swarm optimization (QPSO) method for QoS multicast routing. In the proposed method, QoS multicast routing is converted into an integer programming problem with QoS constraints and is solved by the QPSO algorithm combined with loop deletion operation. The QPSO-based routing method, along with the routing algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA), is tested on randomly generated network topologies for the purpose of performance evaluation. The simulation results show the efficiency of the proposed method on QoS the routing problem and its superiority to the methods based on PSO and GA. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a Binary Particle Swarm Optimization based method to accomplish optimal location and size of a Photovoltaics Grid-Connected System (PVGCS) for distributed power generation. The main technical constraint is the maximum installed peak power, which is limited for utilities Power Distributor Company. The fitness function to be optimized is the profitability index. A fair comparison between the proposed algorithm and other methods is performed. For such goal, convergence curves of the average profitability index versus number of iterations are computed. The proposed algorithm reaches a better solution than genetic algorithms when considering similar computational cost (similar number of evaluations). [Copyright &y& Elsevier]

Abstract: This paper deals with estimation of electricity demand of Iran based on economic indicators using Particle Swarm Optimization (PSO) Algorithm. The estimation is based on Gross Domestic Product (GDP), population, number of customers and average price electricity by developing two different estimation models: a linear model and a non-linear model. The proposed models are obtained based upon available actual data of 21years; since 1980–2000. Then the models obtained are used to estimate the electricity demand of the target years; for a period of time e.g. 2001–2006 and the results obtained are compared with the actual demand during this period. Furthermore, to validate the results obtained by PSO, genetic algorithm (GA) is applied to solve the problem. The results show that the PSO is a useful optimization tool for solving the problem using two developed models and can be used as an alternative solution to estimate the future electricity demand. [Copyright &y& Elsevier]

Abstract: In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO–DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO–DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm. [Copyright &y& Elsevier]

Abstract: The paper presents an optimal fuzzy logic control algorithm for vibration mitigation of buildings using magneto-rheological (MR) dampers. MR dampers are semi-active devices and are monitored using external voltage supply. The voltage monitoring of MR damper is accomplished using evolutionary fuzzy system, where the fuzzy system is optimized using evolutionary algorithms (EAs). A micro-genetic algorithm (-GA) and a particle swarm optimization (PSO) are used to optimize the FLC parameters. Two cases of optimal FLCs are shown. One where FLC is optimized keeping the rule base predefined and in the other case, FLC rule base is also optimized along with other FLC parameters. The FLC rule base and membership function parameters are optimized using 10 variables. Fuzzy controllers with a predefined rule base and with an optimal rule base are applied to a single degree of freedom (SDOF) and a multi-degree of freedom (MDOF) system. Finally, the study evaluates the performance of the fuzzy controller optimized off-line, on a three storey building model under seismic excitations. The main advantage of using FLC to drive the MR damper voltage is that it provides a gradual and smooth change in voltage. Consequently, the present approach provides a better vibration control for structures under earthquake excitations. [Copyright &y& Elsevier]

• A general algorithm framework for hardware software partitioning problem is proposed based on evolutionary algorithms. • An effective method to deal with the infeasible solution of hardware software partitioning problem is proposed • The performances of genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE), and group theory based optimization algorithm (GTOA) for solving hardware software partiti on ing problem are compared. • According to the comparison results, it is pointed out that GTOA and BPSO are more suitable for solving hardware software partitioning problem than GA and HBDE Hardware/software partitioning (HW/SW) is a significant problem in hardware-software co-design, and it is also an NP-hard problem. In order to solve the HW/SW quickly and effectively by evolutionary algorithms, the HW/SW is firstly regarded as a variant of knapsack problem. Based on a new greedy strategy, a greedy repair and optimization algorithm GROM is proposed to eliminate the infeasible solutions. Subsequently, a general algorithm framework based on discrete evolutionary algorithm for HW/SW problem is proposed. On the basis of the above algorithm framework, genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE) and group theory-based optimization algorithm (GTOA) are used to solve large-scale HW/SW instances. The feasibility and effectiveness of the algorithm framework proposed in the paper are verified by comparing the good and bad of the calculation results of above algorithms, and pointed out that the performance of GTOA and BPSO is better than that of HBDE and GA, they are more suitable for solving large-scale HW/SW problem. [ABSTRACT FROM AUTHOR]

Abstract: Affine invariant matching of broken image contours with model shapes is an important but difficult research topic in computer vision. One of the effective approaches to date encapsulates the process as an optimization problem which determines, with the use of a Simple Genetic Algorithm (SGA), the best matching score between pairs of object boundaries. Despite the moderate success of methods developed in this direction, the overall success rate is generally low and inconsistent amongst test trials. This unfavorable outcome could be due to the lack of adequate exploitation in an enormous and erratic search space, which is rather common in the context of shape matching. In this paper, a novel scheme based on Particle Swarm Optimization (PSO) is presented to overcome these problems. Experimental results reveal that the proposed method has outperformed SGA and Real Coded Genetic Algorithm (RCGA) in terms of speed, stability and success rate. In addition, the evolutionary behavior of PSO also permits the use of repeated trials to further enhance the success rate towards perfection with relatively fewer iterations. [Copyright &y& Elsevier]

Abstract: The haplotype reconstruction based on aligned single nucleotide polymorphism (SNP) fragments is to conclude a pair of haplotypes from located polymorphism data. Known computational model of this problem is minimum error correction (MEC) that has been proved to be NP-complete by Lippert et al., but there are few practical algorithms for it. In this paper, we design a heuristic algorithm based on particle swarm optimization (PSO) which was proposed by Kennedy and Eberhart to solve the problem. Extensive computational experiments indicate that the designed PSO algorithm achieves a higher accuracy than the genetic algorithm (GA) designed by Ruisheng Wang to the MEC model in most cases. [Copyright &y& Elsevier]

Abstract: This paper presents a new clustering approach based on the combinatorial particle swarm optimization (CPSO) algorithm. Each particle is represented as a string of length n (where n is the number of data points) the ith element of the string denotes the group number assigned to object i. An integer vector corresponds to a candidate solution to the clustering problem. A swarm of particles are initiated and fly through the solution space for targeting the optimal solution. To verify the efficiency of the proposed CPSO algorithm, comparisons with a genetic algorithm are performed. Computational results show that the proposed CPSO algorithm is very competitive and outperforms the genetic algorithm. [Copyright &y& Elsevier]

Abstract: The resource allocation problem seeks to find an optimal allocation of a limited amount of resource to a number of activities for optimizing the objective under the resource constraint. Most existing methods use mathematical programming techniques, but they may fail to derive exact solutions for large-sized problems with reasonable time. An alternative is to use meta-heuristic algorithms for obtaining approximate solutions. This paper presents a particle swarm optimization (PSO) algorithm for conquering the nonlinear resource allocation problem. To ensure the resource constraint is satisfied, we propose adaptive resource bounds for guiding the search. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. The convergence behavior of the proposed method is analyzed by observing the variations of particle entropy. Finally, a worst-case analysis is conducted to provide a reliable performance guarantee. [Copyright &y& Elsevier]

Abstract: This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles’ information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The convergence property of the proposed IPSO method is analyzed using standard results from the dynamic system theory and some guidelines are derived for proper algorithm parameter selection. A new adaptive strategy for choosing parameters is also proposed to assure convergence of IPSO method, and the proposed algorithm adopts the orthogonal design to generate initial population that are scattered uniformly over feasible solution space. Furthermore, this method combines relaxation technique to zero-one variable and penalty function method to transform the problem to a nonlinear continuous variable optimization one by taking into account more constraints. The feasibility of the proposed method is demonstrated from 10 to 100 unit systems, and the test results are compared with those obtained by Evolutionary Programming (EP) and Genetic Algorithm (GA) in terms of solution quality and convergence properties. The simulation results show that the proposed method is capable of obtaining higher quality solutions. [Copyright &y& Elsevier]

• We propose a novel metaheuristic based on vapor-liquid equilibrium (VLE) process. • We present a new binary programming model for the PBAP modeled via the COP technique. • We perform a large composition between VLE and different already reported methods. Bio-inspired computing is an emerging paradigm which is based on the basics and inspiration of natural phenomena to design new and robust competing techniques. Various nature science areas have motivated the inspiration for the design of new intelligent systems. Chemical engineering is one of them. In Chemistry, the vapor-liquid equilibrium process describes the distribution of chemical species combining two essential phases: vapor phase and liquid phase. Using a binary system of compounds is possible to simulate a search process based on the equilibrium between both phases. In this paper, we propose a new algorithm inspired by this chemical phenomenon for solving a new patient bed assignment problem. This problem consists of assigning patients to beds by considering relevant medical requirements trying to maximize the most covered soft constraints. For that, we take a traditional model, and we transform it by using the constraint optimization paradigm. We test our algorithm on 30 benchmarks taken of Chilean health services. To verify results, we perform statistical comparatives with artificial bee algorithm, ant colony optimization, the bat method, cuckoo search, genetic algorithm, particle swarm optimization, and a random strategy. Computational experiments illustrate that the VLE algorithm properly solved 30 instances, finding all global optimal. In nineteen instances, VLE converged towards the best solution in its median value. In ten instances, the median, the average and the best value, all of them achieved the global optimal. Now, when comparing VLE against other techniques, we can note that VLE is surpassed by the artificial bee colony in two instances only. The rest of the results show VLE as a robust algorithm able to suppress classical, such as genetic algorithm and particle swarm optimization. [ABSTRACT FROM AUTHOR]

• Present the effective transmission positional workspace (ETPW) of Rec4 mechanism. • Construct the constrained optimization model to maximize the volume of ETPW. • Differential evolution is used to solve the constrained optimization problem. A 4-RUP a R parallel mechanism (named as Rec4) with Schöenflies motion has been taken as the research object, and its dimensional synthesis problem has been solved by intelligence algorithms. Using screw theory as a mathematical tool, this paper establishes two indices (i.e., the input transmission index and the output transmission index) to evaluate motion and force transmission performance of mechanism, and presents the definition and numerical calculation method of effective transmission positional workspace (ETPW). A constrained optimization model is constructed to formulate the design problem of dimensional parameters on the maximizing volume of ETPW, and differential evolution (DE) algorithm is employed to solve this problem. A real case of dimensional optimization design is given, and the results show that the proposed model and algorithm are feasible and effective. Furthermore, a comparative test on global optimization capability to solve the dimensional design case is carried out with genetic algorithm (GA), particle swarm optimization (PSO), and DE algorithm, and the numerical experiments demonstrate that DE outperforms GA and PSO in terms of performance indices. [ABSTRACT FROM AUTHOR]

Due to the complexity of their working conditions, historical rolling bearing datasets are mostly limited and imbalanced. The fault data may be composed of multiple subclusters; that is, the historical rolling bearing data have both between-class and within-class imbalances. While support vector machines (e.g., least squares support vector machines (LS-SVMs)) offer advantages when dealing with limited data, traditional fault diagnosis using an LS-SVM has the disadvantages of easy failure of complex imbalanced data and large dependence on the classifier hyperparameters. Therefore, this paper presents a new imbalanced fault diagnosis framework based on a cluster-majority weighted minority oversampling technique (Cluster-MWMOTE) and a moth-flame optimization (MFO)-based LS-SVM classifier. As an extension of MWMOTE, our proposed Cluster-MWMOTE combines the clustering algorithm represented by agglomerative hierarchical clustering (AHC) with MWMOTE. Unlike MWMOTE, Cluster-MWMOTE can avoid the ignoring of small subclusters of faulty (minority) instances far from normal (majority) instances. That is, Cluster-MWMOTE further improves the adaptation to within-class imbalances. As a novel heuristic intelligent algorithm, MFO exhibits faster convergence and higher precision than the traditional optimization algorithms (e.g., particle swarm optimization (PSO) and genetic algorithm (GA)). Therefore, we utilize MFO to optimize the hyperparameters (Sigma & γ) of the LS-SVM classifier for the first time. The fault diagnosis results represented by CWRU and IMS bearing data suggest that the proposed framework provides higher fault diagnosis recognition rates and algorithm robustness than 16 existing algorithms. [ABSTRACT FROM AUTHOR]

This paper studies an intelligent maritime search and rescue (SAR) system problem. According to historical accidents and available SAR equipment information, a bi-level mixed-integer programming (MIP) model is proposed to determine the type and number of SAR equipment allocated to activated stations. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) algorithm are applied to solve the proposed mathematical model. Computational experiments based on real instances in the East Sea China not only validate the effectiveness of the bi-level MIP model in balancing two objectives during decision process, but also indicate that PSO algorithm is better than GA algorithm to solve the proposed model and generate reasonable equipment allocation plans. Some managerial implications are also outlined on the basis of the numerical experiments. [ABSTRACT FROM AUTHOR]

The resource constrained project scheduling problem (RCPSP) has a wide variety of practical applications in construction, manufacturing, project planning, and other areas. Since the 1960s many optimization algorithms have been proposed to solve this NP-hard problem, and their performances are evaluated in well-known test problems with different complexities. Although it is desirable to find an algorithm which can provide promising solutions with reasonable computational efforts for any problem under consideration, no single algorithm can meet that condition. To deal with this challenge, we present a genetic algorithm based memetic algorithm (MA) for solving RCPSP. The algorithm is initiated by a critical path-based heuristic and a variant of the Nawaz, Enscore, and Ham (NEH) heuristic. The algorithm involves a similar block order crossover and a variable insertion based local search. An automatic restart scheme is also presented which assists the algorithm to escape from local optima. In addition, a design-of-experiment (DOE) method is used to determine the set of suitable parameters for the proposed MA. Numerical results, statistical analysis and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed approach. • This paper developed a Genetic algorithm based Memetic Algorithm (MA) for solving classical RCPSPs. • This work proposed an effective heuristic to initialize MA. • Our proposed algorithm was evolved by effective crossover and mutation operators, local search and a restart scheme. • The proposed MA outperformed many state-of-the-art algorithms in PSPLIB and RG benchmarks. • The proposed algorithm showed promising performance in solving practical problems. [ABSTRACT FROM AUTHOR]

This paper proposes an approach based on a weighted L 1 -norm optimization criterion and employs it in conjunction with salp swarm algorithm (SSA) to design 2nd- to 4th-order wideband infinite impulse response (IIR) digital differentiators (DDs). Integration of the proposed fitness function and SSA allows both the magnitude and phase responses to be improved. An extensive simulation is carried out to explore the performance of the proposed approach. First, a comparative study with widely used methods, namely, real-coded genetic algorithm (RCGA) and particle swarm optimization (PSO), is performed to examine accuracy, robustness, consistency and efficiency. Afterwards, the magnitude and phase responses of the proposed digital differentiators are compared with those of the existing designs in the literature. New wideband IIR digital integrators (DIs) are derived by inverting transfer functions of their respective digital differentiators and then compared with the existing designs in the literature in terms of the magnitude and phase responses. Simulation and statistical results reveal that the superiority of the proposed approach is statistically significant and demonstrate that the proposed digital differentiators and integrators significantly outperform all state-of-the-art designs in terms of the magnitude response as measured by the absolute relative error (A R E) with almost linear phase responses in wideband frequency regions. [ABSTRACT FROM AUTHOR]