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People's lives are undergoing tremendous changes with the development of the times. Compared with the past, people's pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education. [ABSTRACT FROM AUTHOR]

This paper proposes a new reentry trajectory generation method, which mainly comprises two parts: reentry profile design and trajectory optimization. Different from conventional methods that design angle-of-attack (AOA) and bank-angle profiles, a height-range profile is proposed in this paper. Using this profile, the AOA can be directly derived and the quasi-equilibrium glide condition is easily satisfied. In addition, the bank angle is analytically derived with the heading error, with only one reversal. Finally, the original problem is transformed into a parameter optimization problem and solved by an improved particle swarm optimization (PSO) method. In PSO, a comparison principle is proposed to tackle constraints and three modifications are applied to the swarm evolution to enhance the searching capacity. To conquer disturbances, an online profile updating strategy is developed based on the nominal profile, so complicated work of designing a trajectory tracker is avoided. Simulation validates the effectiveness of the proposed method and demonstrates that the improved PSO is comparable to the pseudospectral method and is better than the basic PSO. [ABSTRACT FROM AUTHOR]

This paper presents an optimal design of low noise amplifier (LNA) using an efficient swarm-based optimizer called firefly algorithm (FA). Many researchers have used firefly algorithm to solve various nonlinear engineering problems and reported outstanding results. In view of this, FA is implemented for the first time in this paper to optimize the parameters of LNA like gain and noise figure (NF). Two case studies have been performed which includes the minimization of NF and maximization of gain. Optimization of these two parameters has been carried out by considering each parameter as a single objective function. Penalty factor method is considered for handling the constraints. Other parameters of LNA like power consumption, linearity, and stability are also discussed for both the cases. The designed LNA has a cascode structure with inductive source degeneration topology and is implemented in UMC 0.18-μm CMOS technology using CADENCE software. LNA is designed for 5.5-GHz frequency. The performance of FA in optimizing the parameters of LNA is also compared with the performance of other similar contemporary algorithms like particle swarm optimization (PSO), human behavior PSO (HB-PSO), backtracking search algorithm, and cuckoo search algorithm (CSA). The optimized value of LNA parameter using FA and other algorithms when simulated in MATLAB environment is compared with the simulated result of CADENCE. Statistical analysis is also performed for each case study, and the results are compared with the above-mentioned optimization algorithms. Simulation results, comparative study, and statistical analysis confirm the superiority of FA over other methods in terms of its computational efficiency, consistency, and robustness. [ABSTRACT FROM AUTHOR]

Nowadays, the optimization algorithm used in the ship design is a hot research area. The computation efficiency and stability of the algorithm depended on the number of design variables, the algorithm parameters, and operation process. In order to improve design efficiency and stability, this paper focuses on the waterlines approximation with as less control parameters as possible applying the improved quantum-behaved particle swarm optimization (QPSO) algorithm based on NURBS (Non-Uniform Rational B-Spline). The optimization model is constructed with the homogeneous coordinate component of the fitted waterline's control points as the design variables. The objective function is set to minimize the maximum relative difference between the widths of the approximated waterlines and the original ones corresponding to the same stations. The appropriate constraints are set according to the characteristics of the free segments of the waterline. The QPSO algorithm is used to solve this optimization problem. With view to the correlation among the design variables, the algorithm is improved by introducing the memory factor to ensure the design variables within the feasible region during the evolution process. Based on this improved QPSO algorithm, the waterlines of the full-scale hull form are approximated and the hull surface is constructed using the skinning algorithm with them. Compared with the related reference results, this approximation method has higher precision and can satisfy the engineering design requirements. It is indicated that it is feasible to represent the hull form using the reduced data. Furthermore, the success rate and the average relative difference comparison between the basic QPSO and the improved QPSO show the reliability and stability of the algorithm. This method proposed in this paper provides a foundation for the hull surface design based on the reduced data. Meanwhile, the improved QPSO algorithm can be used in the similar optimization problem with the design variables correlated with each other. [ABSTRACT FROM AUTHOR]

This paper proposes a new Multiple-PSO based Membrane Algorithm (M_PSO_MA), which is a one-layered parallel and distributed membrane structure with seven different membranes, i.e., a skin membrane containing six elementary membranes using update rules of different PSOs. The idea is inspired from Frankenstein's PSO (Oca et al. in IEEE Trans Evol Comput 13:1120-1132, 2009) which shows that by integrating components in novel ways effective optimizers can be designed. M_PSO_MA, is designed using the variants of PSO as given in Oca et al. (2009), which although are not hybridized but are applied simultaneously on the same population set. Each elementary membrane sends the best solution and the corresponding particle to the skin membrane, where the best solution is determined among all the collected solutions, which happens to be the best solution of the complete swarm. This global best solution and the corresponding particle are then communicated to all other membranes by the skin membrane besides retaining a copy for itself. The proposed algorithm, M_PSO_MA provides good quality solutions, but may sometime require slightly additional computational time. Therefore, its modified variant namely, MM_PSO_MA is also presented with an objective to minimize the computational time. Both, the proposed variants are compared with the existing best membrane algorithm determined in Singh et al. (Appl Math Comput 246:546-560, 2014) on the basis of CEC-2014 benchmark optimization problem set. In the second part of our paper M_PSO_MA and MM_PSO_MA are applied to solve a real life problem of Iris classification and the results are compared with Mutated PSO, namely MO-MPSO (Wang et al. in Int J Innov Comput Inform Control 9:2963-2977, 2013). The extensive results prove the supremacy of M_PSO_MA over other PSO based Membrane Algorithms. It is concluded that the proposed algorithm can be effectively used for solving complex real life problems as well. [ABSTRACT FROM AUTHOR]

In order to improve the stability and sensitivity of particle swarm optimization (PSO) algorithm and to solve the problem of premature convergence, in this research, a computer PSO algorithm based on thermodynamic motion mechanism is proposed based on the principle of thermodynamic motion mechanism. Firstly, the thermodynamic motion phenomenon, the diffusion law in kinematics and the standard PSO algorithm are introduced. Then, according to the basic idea of thermodynamic motion mechanism, the standardized PSO algorithm is optimized and its optimization process is introduced. Finally, the experimental results are analysed by setting the test function. The results show that among the five test functions, the computer PSO algorithm based on thermodynamic motion mechanism has a higher probability of jumping out of the local optimal solution. Its robustness and stability are much better than standard PSO algorithms. The evolution ability of the computer PSO algorithm based on thermodynamic motion mechanism is better than that of the standard PSO algorithm. The standard PSO algorithm is superior because it is based on thermodynamic motion mechanism. The research in this paper can provide good guidance for improving the performance of PSO algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces an optimal control strategy of model-based predictive control (MPC) based on multiobjective particle swarm optimization (MOPSO) for a sensorless vector control induction motor, which is used in a fuel cell electric vehicle drive system. The proposed MPC-MOPSO algorithm is implemented to tune the weighting parameters of the MPC controller to tackle all the conflicting objective functions. The paper handles the following fitness functions: minimizing the speed error, minimizing the torque ripple, minimizing the DC-link voltage ripple, and minimizing machine flux ripple. Computer simulations studies have been completed utilizing MATLAB/Simulink with a specific end goal of assessing the dynamic performance of the proposed MPC-MOPSO optimal controller and comparing it with single-objective particle swarm optimization and traditional PI controllers. The simulation results demonstrate the good dynamic response of the proposed MPC-MOPSO optimal tuning strategy over the traditional PI controllers for more accurate tracking performance through the whole speed range, especially at starting conditions and load change disturbances. [ABSTRACT FROM AUTHOR]

The paper presents a study on the efficiency of Ant Colony Communities (ACC) used to solve the Travelling Salesman Problem. The ACC is an approach to parallelize the Ant Colony Optimization algorithm (ACO). An ACC is made up of a Community Server that coordinates the work of a set ant colony clients. Each client implements a classical ACO algorithm. The individual colonies process cargos of data obtained from the server and send them back the as partial results. The paper presents a general description of the ACC concept and describes in details two ways of implementing it. The first one uses an inhomogeneous environment of traditional computers working in an asynchronous mode. The second one uses the homogenous Hadoop environment and the processing is done in a synchronized mode. The performance of the Communities is estimated by low level measures: their power and scalability. The high level measure deals with the length of obtained routes. The paper presents also the taxonomy of parallel implementations of the Ant Colony Optimization. [ABSTRACT FROM AUTHOR]

In many machine learning or patter recognition tasks such as classification, datasets with a large number of features are involved. Feature selection aims at eliminating the redundant and irrelevant features which would bring computational burden and degrade the performance of learning algorithms. Particle swarm optimization (PSO) has been widely used in feature selection due to its global search ability and computational efficiency. However, PSO was originally designed for continuous optimization problems and the discretization of PSO in feature selection is still a problem which needs further investigation. This paper develops a novel feature selection algorithm based on a set based discrete PSO (SPSO). SPSO employs a set based encoding scheme which makes it able to characterize the discrete search space in feature selection problem. It also redefines the velocity term and the corresponding arithmetic operators which enables it to search for the optimal feature subset in the discrete space. In addition, a novel feature subset evaluation criterion based on contribution rate is proposed as the fitness function in SPSO. The proposed criterion does not need any pre-determined parameter to keep the balance between relevance and redundancy of the feature subset. The proposed method is compared with six filter approaches and four wrapper approaches on ten well known UCI dataset and the experimental results demonstrate the proposed method is promising. [ABSTRACT FROM AUTHOR]

The large-scale software module clustering problems (SMCPs) are very difficult to solve by using traditional analytical/deterministic-based optimization methods due to their high complexity and computation cost. Recently, particle swarm optimization (PSO) algorithm, a non-deterministic meta-heuristic search algorithm, gained wide attention and has been adapted to address the various large-scale science and engineering optimization problems. However, the applicability and usefulness of PSO algorithm have not been studied by any researcher till date to solve the SMCPs. In this paper, we introduce PSO-based module clustering (PSOMC), which partitions software system by optimizing: (1) intracluster dependency, (2) intercluster dependency, (3) a number of clusters, and (4) a number of module per cluster. To this contribution, we redefine the terms “position” and “velocity” of original PSO under the discrete scenario that best suited to SMCPs. To demonstrate the performance of the proposed approach, extensive experiments on six real-world SMCPs are carried out. We also compare our approach with existing state-of-the-art software module clustering meta-heuristic approaches (group genetic algorithm, hill climbing, and simulated annealing algorithm). The experimental results show that the proposed approach is effective and promising for solving SMCPs. [ABSTRACT FROM AUTHOR]

Performance of particle swarm optimization technique is highly influenced by the population topology. It determines the way in which particles communicate and share information within a swarm. If path length is too small, it implies that a particle communicates with other particles in its close proximity leading to exploitation. On the contrary, if path length is large then the particle interacts with other remote particles leading to exploration. There needs to be a balance between exploration and exploitation and Small world network fits to this need of ours. In this paper, dynamic small world network has been proposed with the objective to have a balanced trade-off between exploration and exploitation. In order to make learning process dynamic linearly decreasing inertia weight has been employed. Experimental study is performed on a set of 23 test functions using different performance evaluation measures. Results obtained are compared with other state of the art techniques demonstrating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the problem of utilizing a low Earth orbit (LEO) infrared constellation in order to track the midcourse ballistic missile. Due to the limited sensor resources of LEO constellation, an appropriate sensor management strategy is necessary. The posterior Cramér-Rao lower bound (PCRLB) is used as the basis for sensor management of LEO constellation. Moreover, a modified binary particle swarm optimization (MBPSO) algorithm is presented for use in finding the optimal allocation scheme of sensors for maximizing the tracking accuracy. The proposed MBPSO algorithm adopts a different transfer function and a new method to generate initial population. The MBPSO’s performance and the PCRLB-based sensor management strategy are verified by applying to a case study. [ABSTRACT FROM AUTHOR]

Parameter estimation is a fundamental research issue which has attracted great concern in the control and synchronization of chaotic systems. This problem can be mathematically described as a multi-dimensional continuous optimization problem through constructing an appropriate fitness function, and then solved via meta-heuristic algorithms. A hybrid flower pollination algorithm is proposed in this paper for solving this problem more efficiently. The proposed algorithm well combines the good global exploration ability of the original flower pollination algorithm and the powerful local exploitation ability of the Nelder-Mead simplex method together by integrating these two methods in a very simple way. The experimental results tested on three typical chaotic system parameter estimation problems with three unknown system parameters, including the Lorenz system, the Rossler system, and the Lorenz system under the noise condition, demonstrate that the algorithm can estimate the unknown parameters efficiently and accurately. The comparisons with the basic flower pollination algorithm and some other four reported methods including the quantum particle swarm optimization, the hybrid adaptive cuckoo search optimization algorithm, the oppositional backtracking search optimization algorithm, and the hybrid algorithm combining differential evolution with artificial bee colony suggest that the proposed algorithm is energy efficient and superior. The proposed algorithm can be used as a new and effective choice for parameter estimation of chaotic systems. [ABSTRACT FROM AUTHOR]

Summary: Cooperative communication is one of the fastest growing research areas of today. It can efficiently mitigate the effect of shadowing and fading with the help of relays and proper relay selection technique. In this paper, a novel relay selection scheme combined with artificial noise (AN) is devised to enhance the secrecy of cooperative networks with amplify‐and‐forward scheme, over Rayleigh fading channels in the presence of a passive eavesdropper. The probability of path selection of ant colony optimization algorithm is used for selecting the best relay with high end‐to‐end signal‐to‐noise ratio. The probability of choosing a path depends on the significance of channel gain (G) and fading coefficients (h). The proposed algorithm finds the best relay in the following wireless scenarios: when (i) both channel gain and fading coefficients are significant; (ii) only fading coefficients are significant; and (iii) only channel gain is significant. Because the direct links between source and destination and source and eavesdropper are considered, AN along with the information is sent by both the source and the selected relay. The performance is evaluated based on secrecy rate (Rs); for the relays randomly placed between the source and destination and for different eavesdropper's location. The results show that the proposed relay selection scheme achieves better secrecy for different wireless scenarios compared with traditional schemes. With the help of AN, the secrecy rate can be made positive even when the eavesdropper lies near to source. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to provide an optimization algorithm for a large-span suspendome which is a spatial prestressed structure with complex mechanical characteristics. The algorithm optimizes the cable tension, the dimension of components, the shape parameters of structure simultaneously. Design/methodology/approach – With the span-to-rise ratio, the length of the strut, the cable tension, the cross-sectional area of the cables and the cross-sectional size of steel members as design variables and the gross mass of entire structure as the objective function, a mathematical optimization method was proposed in the paper based on the hybridization of full stress and particle swarm optimization (PSO). Findings – By using the improved PSO algorithm, the coupling problem of the three types of design variables was resolved: the cable tension and the size and shape of the structure were optimized simultaneously for a suspendome. A program was compiled according to this method and was used for a large-span suspendome. The optimization results of the suspendome demonstrates that the method proposed in the paper has the advantages of high efficiency, rapid convergence, and general applicability, which enable it to be used for the optimization of various types of prestressed steel structures. Originality/value – The optimization program has more general parameters, which can be used to optimize suspendome with different spans, different lattice divisions, and different cable-layouts. In addition to the strength of steel and cable, the integral stability of the members, the deformation of the structure and the geometrical and material nonlinearity were considered in this algorithm and program. The optimization result was compared to the design of an actual large-span suspendome engineering project. [ABSTRACT FROM AUTHOR]

The focus of this paper is on devising designer-friendly hull-form variations coupled with optimization algorithms. Hull-form variations are carried out through parametric modification functions. Two kinds of representative optimization algorithms are considered here. One is the well-known sequential quadratic programming which is the derivative based. The other is particle swarm optimization which is the derivative free. The results applying these two algorithms to typical hull-form optimization problems are discussed in the paper. The technique using the parametric modification functions has been developed for modifying the ship's geometry according to the widely recognized naval architect's design practice. An original geometry can be easily deformed through the change of the variables of the modification functions; and useful information about the effect of the parameters is immediately obtained. Moreover, the variables of the modification functions can be considered as the design variables in the formulation of the optimization problem. For the performance prediction of the hull form, WAVIS version 1.3 is used for the potential-flow and RANS solver. Computational results for both single- and multi-objective problems are presented. [ABSTRACT FROM AUTHOR]

To ensure effective hull assembly line production, it is vital to consider the problems on delivery date of order and sequencing complexity within mixed-model production environments. In this paper two criteria are considered for stratified optimization according to their importance: to minimize the satisfaction ratio of delivery date, and to minimize the complexity degree of the system arising from its state frequent changes. Finding an optimal solution for this complicated problem in reasonable computational time is cumbersome. Therefore, this paper presents an improved particle swarm optimization (IPSO) algorithm to solve the multi-objective sequencing problems. Instead of modeling the positions of particles in a continuous value manner in traditional PSO, IPSO uses an encoding and decoding scheme of task-oriented assignment for representing the discrete input sequences of products. Furthermore, dynamic mutation operator and chaos strategy are introduced to help the particles escape from local optima and the strategy for population decomposition is proposed to further improve the efficiency of the optimization. Numerical simulation suggests that the proposed IPSO scheduler can provide obvious improvement on solution quality and running time. Finally, a case study of the optimization of a panel block assembly line was given to illustrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper proposes an improved gravitational search algorithm (IGSA) to find the optimum solution for short-term economic/environmental hydrothermal scheduling (SEEHTS), which considers minimizing fuel cost as well as minimizing pollutant emission. In order to improve the performance of GSA, this paper firstly uses particle memory character and population social information to update velocity. Secondly, a chaotic mutation operator is embedded into GSA and a selection-operator-based greedy rule is adopted to update population. When dealing with the constraints of the SEEHTS, a modification strategy by dividing the violation water volume into several parts and randomly selecting intervals to adjust the water discharge gradually is proposed to handle the water dynamic balance constraints. Meanwhile, a new symmetrical adjusting strategy is adopted to handle reservoir storage constraints. Furthermore, the priority index strategy based on thermal power output is applied to handle system load balance constraints. To test the performance of the proposed method, simulation results have been compared with those obtained by particle swarm optimization, evolutionary programming and differential evolution reported in literature. The results show that the proposed IGSA provides the optimum solution with less fuel cost and smaller emission. So it demonstrates that IGSA is effective for solving SEEHTS problem. [ABSTRACT FROM AUTHOR]

Slack variables are utilized in optimisation problems in order to build soft margin classifiers that allow for more flexibility during training. A robust binary classification algorithm that is based on the minimisation of the energy of slack variables, called the Mean Squared Slack (MSS), is proposed in this paper. Initially, the algorithm is analysed for the linear case, where the minimum mean squared slack is attained as a separating vector. Next, the kernel trick is exploited to facilitate computation of non-linear separating hyperplanes. For this paper, two kernels are tested, namely the radial basis function (RBF) and the polynomial kernel. In order to ensure a time and memory efficient system that converges in a few iterations four strategies are applied so as to withhold just a subset of feature vectors that are misclassified during training. Aiming to the automatic optimisation of the kernel parameters a modern combination of particle swarm optimisation (PSO) with artificial immune system (AIS) is tested. The aforementioned evolutionary methods are combined in a parallel architecture. Four datasets of diverse nature are exploited for performance evaluation, namely the iris, the SPECTheart, the vertebral column, and the wine quality datasets. Simulation experiments demonstrate high classification accuracy in a number of benchmark datasets. [ABSTRACT FROM AUTHOR]

Some real-world optimisation problems are dynamic; that is, their objective function and/or constraints vary over time. Solving such problems is very challenging. Particle swarm optimisation (PSO) is a well-known and efficient optimisation algorithm. In this paper, the PSO variants, devised for dynamic optimisation problems, are reviewed. This is the first comprehensive review that is conducted on PSO variants in dynamic environments. The author believes that this paper can be useful for researchers who intend to solve dynamic optimisation problems. [ABSTRACT FROM AUTHOR]

This paper focuses on the impacts of exit strategy in existing fuzzy portfolio selection models. In the securities market, the term 'exit' means that the investor may sell his/her equity on account of some exogenous or endogenous incentives, especially when the security price becomes higher than his/her expectation or lower than his/her tolerance. It is a common problem that all investors need to face in the investment horizon. There have been various studies reported in the current literature employing fuzzy set theory to handle the uncertainty of portfolio selection. Nevertheless, none of these studies considers the influence caused by exit strategy which will be executed once a security price fluctuates out of the expected interval. In this paper, we first secure the future returns of each security by profit/loss exit points (prices) before rebuilding fuzzy portfolio selection models. Next, the properties of exit points are analyzed. Then a meta-heuristic method is proposed to analyze the differences between the new models' experimental results and those of previous methods. Finally, we discuss how to assign proper exit point values to different securities based on different risk attitudes, and apply our approach to a real application on the New York Stock Exchange. [ABSTRACT FROM AUTHOR]

The investigation of water level fluctuations in lakes for protecting them regarding the importance of these water complexes in national and regional scales has found a special place among countries in recent years. The importance of the prediction of water level balance in Lake Urmia is necessary due to several-meter fluctuations in the last decade which help the prevention from possible future losses. For this purpose, in this paper, the performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the lake water level balance has been studied. In addition, for the training of the adaptive neuro-fuzzy inference system, particle swarm optimization (PSO) and hybrid backpropagation-recursive least square method algorithm have been used. Moreover, a hybrid method based on particle swarm optimization and recursive least square (PSO-RLS) training algorithm for the training of ANFIS structure is introduced. In order to have a more fare comparison, hybrid particle swarm optimization and gradient descent are also applied. The models have been trained, tested, and validated based on lake level data between 1991 and 2014. For performance evaluation, a comparison is made between these methods. Numerical results obtained show that the proposed methods with a reasonable error have a good performance in water level balance prediction. It is also clear that with continuing the current trend, Lake Urmia will experience more drop in the water level balance in the upcoming years. [ABSTRACT FROM AUTHOR]

This paper makes an in-depth exploration into the job-shop scheduling problem (JSP). After reviewing the related literature, the local search mechanism of the particle swarm algorithm (PSA) and the largespan search principle of standard cuckoo search algorithm (CSA) were combined into an improved cuckoo search algorithm (ICSA), which is capable of both local search and global search. Later, several simulation experiments were carried out on the LA type typical library proposed by Lawrence, and the stability and accuracy of the ICSA was contrasted with those of the PSA and the genetic algorithm (GA) based on the means and variances in multiple iterations. After the comparison, a convergence analysis of the ICSA was specially designed for our model. The results demonstrate that the ICSA provides a better tool for solving the JSP than other algorithms. The research findings lay a solid theoretical basis for the JSP in the actual production process. [ABSTRACT FROM AUTHOR]

This paper presents a novel Fuzzy-based bending angle predictor in laser bending process. Upon the case, and situations, different input data, membership functions and rules are developed dynamically for the Fuzzy predictor. Our main focus in developing the proposed controller is keeping generality of design. So, the controller can be adapted in different cases easily. To compensate for the possible lack of knowledge of experts for developing rule base of a Fuzzy controller, here, nonlinear regression is used as an alternative approach for developing the rule base. Furthermore, the performance of the controller is improved using particle swarm optimization (PSO) method. Also, based on the proposed Fuzzy controller and PSO, another predictor able to find one possible set of input values to catch a predefined angle is proposed. Several experimental tests were conducted to evaluate performance of the proposed controllers. Comparing experimental and predicted results shows that they are in a proper agreement with our claim. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems. [ABSTRACT FROM AUTHOR]

This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms. [ABSTRACT FROM AUTHOR]

In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO). Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature. [ABSTRACT FROM AUTHOR]

In consideration of the difficulty in determining the parameters of underactuated autonomous underwater vehicles in multi-degree-of-freedom motion control, a hybrid method that combines particle swarm optimization (PSO) with artificial fish school algorithm (AFSA) is proposed in this paper. The optimization process of the PSO-AFSA method is firstly introduced. With the control simulation models in the horizontal plane and vertical plane, the PSO-AFSA method is elaborated when applied in control parameter optimization for an underactuated autonomous underwater vehicle. Both simulation tests and field trials were carried out to prove the efficiency of the PSO-AFSA method in underactuated autonomous underwater vehicle control parameter optimization. The optimized control parameters showed admirable control quality by enabling the underactuated autonomous underwater vehicle to reach the desired states with fast convergence. [ABSTRACT FROM AUTHOR]

This paper addresses the problems caused by an inappropriate selection of sliding surface parameters in fuzzy sliding mode controllers via an optimization approach. In particular, the proposed method employs the parallel distributed compensator scheme to design the state feedback based control law. The controller gains are determined in offline mode via a linear quadratic regular. The particle swarm optimization is incorporated into the linear quadratic regular technique for determining the optimal weight matrices. Consequently, an optimal sliding surface is obtained using the scalar $sign$ function. This latter is used to design the proposed control law. Finally, the effectiveness of the proposed fuzzy sliding mode controller based on parallel distributed compensator and using particle swarm optimization is evaluated by comparing the obtained results with other reported in literature. [ABSTRACT FROM AUTHOR]

Consolidation of services is one of the key problems in cloud data centers. It consists of two separate but related issues: Virtual machine (VM) placement and VM migration problems. In this paper, a VMconsolidation scheme is proposed that turns the virtual machine consolidation (VMC) problem into a vector packing optimization problem based on deadlockfree migration (DFM) to minimize the energy consumptions. To solve this NP-hard and computationally infeasible for large data centers problem, a novel algorithm named Chicken Swarm Optimization based on deadlock-free migration (DFM-CSO) algorithm is proposed. The DFM-CSO algorithm is characterized by the 'one-step look-ahead with n-VMs migration in parallel (OSLA-NVMIP)' method, which carries out the VM migration validation and the rearrangement of target physical host, as well as records the migration order for each solution placement, so that VM transfer can be completed according to the migration sequence. The experimental results, for both real and synthetic datasets, show that the proposed algorithm with higher convergence rate is favourable in comparison with the other deadlock-free migration algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we show that a contextual neural network with artificial neurons performing a conditional aggregation of signals can be trained by the generalized backpropagation algorithm. To allow this algorithm to be used for training contextual neural networks, we derive appropriate generalized delta rules. Our approach is constructed on the basis of introduced generalized representation of the aggregation function in an ordered groups space and division of its attention function into binary scan-path and contribution functions. The advantage of the proposed representation is that it clarifies the description of the aggregation process by using Stark's scan-path theory and allows us to achieve results independent from the actual form of the attention functions used during aggregation. As such, the proposed solution is valid for the whole presented family of conditional aggregation functions and is a considerable extension of the previously reported results. In particular, the obtained results are valid for the introduced exemplary attention functions which illustrate performed calculations. Moreover, the presented solution can be further extended by considering real valued, non-binary contribution functions inside ordered aggregation functions. Especially promising are its possible applications in large deep neural networks and energy-limited systems. [ABSTRACT FROM AUTHOR]

How to solve constrained optimization problems constitutes an important part of the research on optimization problems. In this paper, a hybrid immune clonal particle swarm optimization multi-objective algorithm is proposed to solve constrained optimization problems. In the proposed algorithm, the population is first initialized with the theory of good point set. Then, differential evolution is adopted to improve the local optimal solution of each particle, with immune clonal strategy incorporated to improve each particle. As a final step, sub-swarm is used to enhance the position and velocity of individual particle. The new algorithm has been tested on 24 standard test functions and three engineering optimization problems, whose results show that the new algorithm has good performance in both robustness and convergence. [ABSTRACT FROM AUTHOR]

International Electronical Committee (IEC) 61850-90-7 is a part of the IEC 61850 series which specifies the advanced functions and object models for power converter based Distributed Energy Resources (DERs). One of its functions, the Voltage/VAR (V/V) control function, is used to enhance the stability and the reliability of the voltage in the distribution system. The conventional V/V function acts mainly for flattening the voltage profile as for a basic grid support function. Currently, other objectives such as the minimization of line loss and the operational costs reduction are coming into the spotlight. In order to attain these objectives, the V/V function and hence the DER units shall actively respond to the change of distribution system conditions. In this paper, the modification of V/V function and new requirements are proposed. To derive new requirements of V/V function, loss minimization is applied to a particle swarm optimization (PSO) algorithm where the condition of voltage constraint is considered not to deteriorate the voltage stability of the distribution system. [ABSTRACT FROM AUTHOR]

In this paper, an improved particle swarm optimization guidance (IPSOG) is proposed. The particle swarm optimization guidance (PSOG) is presented to solve nonlinear and dynamic missile guidance problems. However, the miss distance (MD) tends to be large. The objective function of the relative distance in the PSOG leads the missile to the current position of a target. Therefore, the PSOG is similar to pursuit guidance. In the IPSOG, a new objective function for the PSOG is introduced to improve the guidance performance. The line-of-sight (LOS) rate is taken as the objective function. The fitness function is then evaluated according to the defined objective function. Numerical simulation results show that the guidance performance of the IPSOG is better than the PSOG. [ABSTRACT FROM AUTHOR]

This paper proposes a dual mode guidance, which is called the proportional navigation-improved particle swarm optimization guidance (PN-IPSOG), in order to optimize the guidance performance of a missile. The improved particle swarm optimization guidance (IPSOG) has the advantages of a robustness and fast reaction, but the attitude of the missile is unstable in the initial phase. Therefore, proportional navigation (PN) guidance is used to adjust the attitude of the missile in the initial phase. Meanwhile, the angle μ that is between the tangent of the current line-of-sight (LOS) rate and the horizontal reference line is calculated. When a set of μ is close to zero, the PN-IPSOG switches from the PN guidance to the IPSOG. Numerical simulation results show that the performance of the PN-IPSOG is better than the PN guidance at miss distance and lateral accelerations. [ABSTRACT FROM AUTHOR]

Fruit fly optimization algorithm (FOA) is a method that we have previously developed from the food-finding behavior of fruit flies to solve optimization problems. The advantage of FOA is simple and easy to understand compared to traditional stochastic algorithms. In this paper, we propose a modified algorithm called novel 3D-FOA. The performances of the 3D-FOA are far better than those of the original FOA. We select more than thirty different nonlinear functions as test vehicles to show that the search efficiency and/or quality of the 3D-FOA is superior to that of the genetic algorithm and particle swarm optimization algorithm. We also apply the 3D-FOA on some economics topics, two theoretic examples and a case study. Our results strongly suggest that the 3D-FOA can enhance capabilities in a variety of fields and future experiments. [ABSTRACT FROM AUTHOR]

SUMMARY In multichannel acoustic echo cancelers based on linear combination, the uniqueness problem exists, namely, that adaptive filters cannot identify the correct echo paths due to highly cross-correlated multichannel input signals. Preprocessing of the input signal is good candidates for solving this problem. However, it can have the drawbacks of degrading audio quality as a secondary problem. In this misadjustment problem, a universal solution is not derived in the conventional adaptive filter's optimization. In this paper, we propose a new multichannel adaptive echo canceling algorithm based on particle swarm optimization (PSO) without preprocessing of the input signals. PSO is a stochastic optimization technique inspired by the social behavior of bird flocking or fish schooling, and this is the first attempt to apply it to multichannel acoustic echo cancelers. By performing various simulations, we confirm that the proposed method can estimate correct echo paths with highly cross-correlated input signals. [ABSTRACT FROM AUTHOR]

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html . [ABSTRACT FROM AUTHOR]

In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific parameters of the electrochemical model of a Lithium-Ion battery with LiCoO 2 cathode chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these parameter changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified model parameters were used to generate the respective battery models for both healthy and degraded batteries. These models were then validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the extended version of the conventional DEED to overcome the ramp rate violations when its optimal solutions for one period (normally one day) are implemented repeatedly and periodically over consequent dispatch periods to meet the periodic load demands. This dynamic dispatch problem, which is referred to as EDEED, is a multi-objective optimization problem which simultaneously minimizes both fuel cost and pollutants emission while satisfying a set of constraints. A multi-objective particle swarm optimization (MOPSO) method has been applied in this article for solving EDEED problem. The performance of the proposed method has been evaluated on the 10-unit test system with non-smooth fuel cost and emission level functions in comparison with those methods reported in the literature. [ABSTRACT FROM AUTHOR]

The Gbest-guided artificial bee colony (GABC) algorithm is a latest swarm intelligence-based approach to solve optimisation problem. In GABC, the individuals update their respective positions by drawing inspiration from the global best solution available in the current swarm. The GABC is a popular variant of the artificial bee colony (ABC) algorithm and is proved to be an efficient algorithm in terms of convergence speed. But, in this strategy, each individual is simply influenced by the global best solution, which may lead to trap in local optima. Therefore, in this paper, a new search strategy, namely “Fully Informed Learning” is incorporated in the onlooker bee phase of the ABC algorithm. The developed algorithm is named as fully informed artificial bee colony (FABC) algorithm. To validate the performance of FABC, it is tested on 20 well-known benchmark optimisation problems of different complexities. The results are compared with those of GABC and some more recent variants of ABC. The results are very promising and show that the proposed algorithm is a competitive algorithm in the field of swarm intelligence-based algorithms. [ABSTRACT FROM PUBLISHER]

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts. [ABSTRACT FROM AUTHOR]

These smaller consumers usually prefer to purchase at a constant price that is adjusted at most a few times per year. Electricity retailers are in business to bridge the gap between the wholesale market and these smaller consumers. This paper proposes a hybrid approach based on binary imperialist competitive algorithm (BICA) and binary particle swarm optimization (BPSO) to find the optimal energy procurement for electricity retailer with multiple procurement options such as self-generating facility, bilateral contracts and pool market purchase in restructured electricity markets. The solutions of these problems provide adequate information to obtain an energy procurement strategy for retailers. Also, the results of proposed hybrid approach are compared with conventional ICA and PSO methods. Test results show that the proposed hybrid approach is more effective and has higher capability in finding optimum solutions in comparison to ICA and PSO methods. A case study is used to illustrate the efficiency of the proposed hybrid approach. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Networks (RBFNNs) have been widely used for classification and function approximation tasks. Hence, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results. This paper presents a new learning method for RBFNNs. An improved algorithm for center adjustment of RBFNNs and a novel algorithm for width determination have been proposed to optimize the efficiency of the Optimum Steepest Decent (OSD) algorithm. To initialize the radial basis function units more accurately, a modified approach based on Particle Swarm Optimization (PSO) is presented. The obtained results show fast convergence speed, better and same network response in fewer train data which states the generalization power of the improved neural network. The Improved PSO–OSD and Three-phased PSO–OSD algorithms have been tested on five benchmark problems and the results have been compared. Finally, using the improved radial basis function neural network we propose a new method for object image retrieval. The images to be retrieved are object images that can be divided into foreground and background. Experimental results show that the proposed method is really promising and achieves high performance. [ABSTRACT FROM AUTHOR]