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Particle swarm optimization algorithm (PSO) has been optimized from various aspects since it was proposed. Optimization of PSO can be realized by optimizing its iterative process or the initial parameters and heuristic methods have been combined with the initial PSO algorithm to improve its performance. In this paper, we introduce the Simplicial Algorithm (SA) of fixed point theory into the optimization of PSO and proposed a FP-PSO (Fixed-point PSO) improved algorithm. In FP-PSO algorithm, the optimization of target function is converted into the problem of solving a fixed point equation set, and the solution set obtained by Simplicial Algorithm (SA) of fixed point theory is used as the initial population of PSO algorithm, then the remaining parameters can be obtained accordingly with classical PSO algorithm. Since the fixed point method has sound mathematical properties, the initial population obtained with FP-PSO include nearly all the approximate local extremes which maintain the diversity of population and can optimize the flight direction of particles, and shows their advantages on setting other initial parameters. We make an experimental study with five commonly used testing functions from UCI (University of California Irvine) which include two single-peak functions and three multi-peak functions. The results indicate that the convergence accuracy, stability, and robustness of FP-PSO algorithm are significantly superior to existing improve strategies which also optimize PSO algorithm by optimizing initial population, especially when dealing with complex situations. In addition, we nest the FP-PSO algorithm with four classical improved PSO algorithms that improve PSO by optimizing iterative processes, and carry out contrast experiments on three multi-peak functions under different conditions (rotating or non-rotating). The experimental results show that the performance of the improved algorithm using nested strategy are also significantly enhanced compared with these original algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we have designed a new optimization technique, which is named as the Improved Multi-verse Algorithm with Levy Flights (ILFMVO) algorithm. The quality of the population is an important factor that can directly or indirectly affect the strength of an algorithm in searching for the given search space for an optimal solution. Also, having an initialization of the initial population with randomly generated candidate solutions is not an effective idea in every case, especially when the search space is large. Hence, we have updated the Levy flights based Multi-verse Optimizer (LFMVO) by dividing initialization into two parts. To investigate the ability of ILFMVO, we have solved a constrained economic dispatch problem with a non-smooth, non-convex cost functions of three, six, and twenty thermal generator systems and two design engineering problems with nonlinear objectives and complex nonlinear constraints. We have compared our results with other standard algorithms. We have presented the sensitivity analysis to check the robustness and stability of our approach. The outcome demonstrated that ILFMVO has better accuracy, stability, and convergence. [ABSTRACT FROM AUTHOR]

In practical operating conditions, the Solar-Photo Voltaic (SPV) system experiences multifarious irradiation and temperature levels, which generate power with multiple peaks. This is considered as the nonuniform operating condition (NUOC). This requires accurate tracking of global power peaks to achieve maximum power from SPV, which is a challenging task. Hence, this paper presents an incremental Conductance based Particle Swarm Optimization (ICPSO) algorithm for accurate tracking of maximum global power from active power multiple peaks generated by the SPV. The proposed algorithm continuously adjusts the individual particle's weight component, which depends on its distance from the global best position during the tracking process. The proposed algorithm has the merit of continuous adjustment of weight components which reduces active power oscillations at the optimal global position area. Proposed ICPSO algorithm has been successfully designed and implemented for Solar-photo voltaic (PV) under nonuniform operating condition. It is established that the proposed algorithm enhances the output power of the Solar-PV up to 7% with the maximum power tracking of 0.1 s compared to other maximum power point tracking algorithms. [ABSTRACT FROM AUTHOR]

Image segmentation has considered an important step in image processing. Fuzzy c-means (FCM) is one of the commonly used clustering algorithms because of its simplicity and effectiveness. However, FCM has the disadvantages of sensitivity to initial values, falling easily into local optimal solution and sensitivity to noise. To tackle these disadvantages, many optimization-based fuzzy clustering methods have been proposed in the literature survey. Particle swarm optimization (PSO) has good global optimization capability and a hybrid of FCM and PSO have improved accuracy over tradition FCM clustering. In this paper, a new image segmentation method based on Dynamic Particle swarm optimization (DPSO) and FCM algorithm along with the noise reduction mechanism is proposed. DPSO has the advantages to change the inertia weight and learning parameters dynamically. It adopts the inertia weight according to the fitness value and learning parameters along with time. The proposed method combines DPSO with FCM, using the advantages of global optimization searching and parallel computing of DPSO to find a superior result of the FCM algorithm. Moreover, a noise reduction mechanism based on the surrounding pixels is used for enhancing the anti-noise ability. The synthetic image and Magnetic Resonance Imaging (MRI) have been used for testing the proposed method by introducing different types of noises and the results show that the proposed algorithm has better performance and less sensitive to noise. [ABSTRACT FROM AUTHOR]

Permutation Flow-Shop Scheduling Problem (PFSP) which exists in many manufacturing systems is a classic combinatorial optimization problem. Studies have shown that the PFSP including more than three machines belongs to the NP-hard problems and is difficult to solve. Based on a new bio-inspired algorithm { Artificial Buttery Optimization (ABO) algorithm, this paper presents a Discrete Artificial Buttery Optimization (DABO) algorithm to find the permutation that gives the smallest completion time or the smallest total ow time. The performance of the proposed algorithm is tested on well-known benchmark suites of Car, Reeves and Taillard. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. The DABO algorithm is then employed for one production optimization problem. [ABSTRACT FROM AUTHOR]

Back analysis for seepage parameters is a classic issue in hydraulic engineering seepage calculations. Considering the characteristics of inversion problems, including high dimensionality, numerous local optimal values, poor convergence performance, and excessive calculation time, a biological immune mechanism-based quantum particle swarm optimization (IQPSO) algorithm was proposed to solve the inversion problem. By introducing a concentration regulation strategy to improve the population diversity and a vaccination strategy to accelerate the convergence rate, the modified algorithm overcame the shortcomings of traditional PSO which can easily fall into a local optimum. Furthermore, a simple multicore parallel computation strategy was applied to reduce computation time. The effectiveness and practicability of IQPSO were evaluated by numerical experiments. In this paper, taking one concrete face rock-fill dam (CFRD) as a case, a back analysis for seepage parameters was accomplished by utilizing the proposed optimization algorithm and the steady seepage field of the dam was analysed by the finite element method (FEM). Compared with immune PSO and quantum PSO, the proposed algorithm had better global search ability, convergence performance, and calculation rate. The optimized back analysis could obtain the permeability coefficient of CFRD with high accuracy. [ABSTRACT FROM AUTHOR]

The abnormalities like (N-1) line contingencies are unavoidable and consequently can cause for either transmission system security margin decrement or power loss increment. Both the issues are primary objectives of the modern power system operation due to their significant effect on generation capacity and economics. This paper proposes Generalized Unified Power Flow Controller (GUPFC) for maintaining adequate security margin and performance improvement under (n-1) contingencies. At first stage, the optimal location of GUPFC device is proposed to determine based on (N-1) line contingency severity index. In second stage, the rating of the GUPFC device is optimized towards minimization of multiobjective function, which formulated using real power loss and security margin index. The parameters involved in the power injection modeling (PIM) of GUPFC and various system operational parameters are optimized using Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) algorithms towards minimization of multi-objective function. The case studies on IEEE 14-bus and IEEE 30-bus test system are validating the capability GUPFC device to minimize transmission loss and improve security margin significantly even under (n-1) line contingency and need of FACTS devices for real-time. [ABSTRACT FROM AUTHOR]

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

This paper presents the solution to mitigate the total harmonic distortion (THD) in multilevel inverters (MLIs) using novel improved whale optimization algorithm (IWOA). The IWOA falls under the category of swarm-based nature inspired optimization algorithms. It uses a novel diffusion process using a random walk technique and utilizes an additional ranking system to estimate the optimum solution to minimize THD. Moreover, THD minimization is further accomplished through nine various meta-heuristic algorithms for investigation and comparative analysis. The selected algorithms along with the proposed IWOA are rigorously tested on single phase 5 and 7 level cascaded H-Bridge MLIs for various performance parameters such as consistency, computational efficiency and speed of convergence. It is found that the proposed algorithm outperforms the nine algorithms and is efficient for THD minimization for modulation index (MI) in the range of 0–1. The results are analyzed and reported after thorough verification using MATLAB simulation. It presents a novel switching technique through improved whale optimization algorithm (IWOA) to optimize the switching angles of a single phase 5 and 7 level multilevel inverters (MLIs) in order to minimize the total harmonic distortion (THD). IWOA incorporates a novel diffusion process and two new position updating techniques The proposed IWOA provides better computational efficiency, improved consistency and faster convergence compared to the older whale optimization algorithm (WOA) with minimal tuning of the algorithm's parameters. The proposed IWOA is compared alongside various meta-heuristics like genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), krill herd (KH), artificial electric field algorithm (AEFA), sun flower optimizer (SFO), galactic swarm optimization (GSO), fruit fly optimization algorithm (FOA) and whale optimization algorithm (WOA) for optimal THD minimization. [ABSTRACT FROM AUTHOR]

The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs; in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm. [ABSTRACT FROM AUTHOR]

To improve convergence speed and search accuracy, this paper proposes an improved quantum-behaved particle swarm optimization algorithm based on Levy flight. The improved algorithm reduces the probability of a local optimal solution through Levy flight and enhances the accuracy of the later search through a postsearch strategy. During the search process, the probability of quantum behavior is retained and the directivity of the particles is strengthened. According to the simulation comparison results, the improved quantum-behaved particle swarm algorithm exhibits faster convergence speed and higher accuracy. [ABSTRACT FROM AUTHOR]

The special features of some old residential complexes make reconstructing a better option than rebuilding. The complexity of redesign issues, the ability of computers to solve problems, and the capability of evolutionary optimization techniques in architecture make the use of these methods a priority over traditional processes. This paper, while introducing Particle Swarm Optimization (PSO) as one of the swarm intelligence based algorithms, answers the question of how this algorithm can be used to redesign residential complexes. To achieve this, after reviewing the application of PSO algorithms in architectural space planning, the redesign criteria and constraints was divided into four categories; including primary design specifications, contact-related criteria, designer-related criteria and redesign-related constraints. After the quantification process of these criteria, how to implement the algorithm to achieve optimal plans for each of the five plan types available in high-rise apartment complex in Mashhad, Iran, was shown as a case study. [ABSTRACT FROM AUTHOR]

Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on the problem to be solved. Firefly algorithm is a suitable method for power system operation scheduling. This paper presents a modified firefly algorithm to address unit commitment issues. Generally, two steps are involved in solving unit commitment problems. The first step determines the generating units to be operated, and the second step calculates the amount of demand-sharing among the units (obtained from the first step) to minimize the cost that corresponds to the load demand and constraints. In this work, the priority list method was used in the first step and the second step adopted the modified firefly algorithm. Ten generators were selected to test the proposed method, while the values of the cost function were regarded as criteria to gauge and compare the modified firefly algorithm with the classical firefly algorithm and particle swarm optimization algorithms. Results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation. [ABSTRACT FROM AUTHOR]

Prediction of bus arrival time is an important part of intelligent transportation systems. Accurate prediction can help passengers make travel plans and improve travel efficiency. Given the nonlinearity, randomness, and complexity of bus arrival time, this paper proposes the use of a wavelet neural network (WNN) model with an improved particle swarm optimization algorithm (IPSO) that replaces the gradient descent method. The proposed IPSO-WNN model overcomes the limitations of the gradient-based WNN which can easily produce local optimum solutions and stop the training process and thus improves prediction accuracy. Application of the model is illustrated using operational data of an actual bus line. The results show that the proposed model is capable of accurately predicting bus arrival time, where the root-mean square error and the maximum relative error were reduced by 42% and 49%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Research (2307-1877) is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the characterization of inclusions in concrete structures, including the number of inclusions, their geometries, and electromagnetic properties. To solve this problem, a two phase algorithm that combines matched-filter-based reverse-time (MFBRT) migration algorithm with the particle swarm optimization (PSO) is employed. The first phase runs the MFBRT that can, robustly, define the number of inclusions and their centers; however, it cannot define the inclusion geometry and electromagnetic properties. Given the results obtained in the first phase, the PSO is launched in the second phase, in a parametric approach, to define the radii of the inclusions and their properties. Three types of inclusions were considered, water, air, and conductor. Results considering a nonhomogenous host medium having from one to three inclusions are presented showing the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposed curve adaptive and simulation annealing grasshopper optimization algorithm to avoid the disadvantages that the grasshopper optimization algorithm was easy to fall into local optimum, slow convergence speed and poor search accuracy. First of all, this paper introduced the adaptive curve to replace the linear adaptation of the grasshopper optimization algorithm key parameter, which improved the global search ability of the algorithm. Then, on this basis, this paper introduced the simulated annealing algorithm, which had a certain probability of receiving the inferior solution of the grasshopper algorithm to jump out of local optimization and achieve global optimization. Adaptive reduction the range of random solutions of grasshopper position in simulated annealing was beneficial to further improve the ability of grasshopper algorithm's exploitation. Through test function, the experimental results show that the improved new algorithm has higher quality and better convergence speed. [ABSTRACT FROM AUTHOR]

Unlike traditional analytical optimization techniques, bio-inspired multi-objective optimization has recently enjoyed an intense interest regarding wireless sensor network (WSN) issues. Network lifetime and target coverage are among the major concerns in many well-established scenarios of WSNs, such as routing and node deployment. For set covers scenario in WSNs, however, little attention has been paid to the role of multi-objective requirements. In this paper, we take a step toward exploring the feasibility of such bio-inspired algorithms for solving multi-objective set covers problem in WSNs. The main contributions of this study are twofold. First, we extend the set covers problem and address it with three issues: network lifetime, target coverage, and network connectivity as a multi-objective set covers (MOSC) formulation. To the best of our knowledge, this is the first effort to define such a general multi-objective set covers problem. Second, we design and elaborate four well-known multi-objective optimization algorithms from evolutionary and swarm intelligence community to tackle the formulated MOSC problem. All characteristic components of the adopted algorithms are tailored specifically to handle the formulated problem. Further, a self-adaptive heuristic mutation operator is proposed to attain and emphasize the strength of the algorithms in terms of network lifetime and coverage probability. Extensive simulations are performed to test and demonstrate the performance of the designed algorithms to tackle the problem appropriately. [ABSTRACT FROM AUTHOR]

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher. [ABSTRACT FROM AUTHOR]

This paper presents a novel evolutionary optimization methodology for multiband and wide-band patch antenna designs. The particle swarm optimization (PSO) and the finite-difference time-domain (FDTD) are combined to achieve the optimum antenna satisfying a certain design criterion. The antenna geometric parameters are extracted to be optimized by PSO, and a fitness function is evaluated by FDTD simulations to represent the performance of each candidate design. The optimization process is implemented on parallel clusters to reduce the computational time introduced by full-wave analysis. Two examples are investigated in the paper: first, the design of rectangular patch antennas is presented as a test of the parallel PSO/FDTD algorithm. The optimizer is then applied to design E-shaped patch antennas. It is observed that by using different fitness functions, both dual-frequency and wide-band antennas with desired performance are obtained by the optimization. The optimized E-shaped patch antennas are analyzed, fabricated, and measured to validate the robustness of the algorithm. The measured less than — 18 dB return loss (for dual-frequency antenna) and 30.5% bandwidth (for wide-band antenna) exhibit the prospect of the parallel PSO/FDTD algorithm in practical patch antenna designs. [ABSTRACT FROM AUTHOR]

In this paper we present two recent metaheuristics, particle swarm optimization and differential evolution algorithms, to solve the single machine total weighted tardiness problem, which is a typical discrete combinatorial optimization problem. Most of the literature on both algorithms is concerned with continuous optimization problems, while a few deal with discrete combinatorial optimization problems. A heuristic rule, the smallest position value (SPV) rule, borrowed from the random key representation in genetic algorithms, is developed to enable the continuous particle swarm optimization and differential evolution algorithms to be applied to all permutation types of discrete combinatorial optimization problems. The performance of these two recent population based algorithms is evaluated on widely used benchmarks from the OR library. The computational results show that both algorithms show promise in solving permutation problems. In addition, a simple but very efficient local search method based on the variable neighbourhood search (VNS) is embedded in both algorithms to improve the solution quality and the computational efficiency. Ultimately, all the best known or optimal solutions of instances are found by the VNS version of both algorithms. [ABSTRACT FROM AUTHOR]

The basic seagull optimization algorithm(SOA) has some drawbacks such as low solution accuracy, slow convergence rate, and easy to fall into local optima when solving complex function optimization problems. This paper proposes an improve SOA based on somersault foraging strategy(abbreviated as SFSOA). First, the proposed SFSOA updates the positions of seagull individuals by using nonlinearly decrease strategy of control parameter A based on the inverted sigmoid function. This way improves individual solution quality and accelerates convergence speed of algorithm. Then, a novel learning mechanism based on the somersault foraging strategy is introduced for the global best individual to increase the position diversity of seagull individual and avoid the algorithm falling into a local optimum at the lately stage. The proposed SFSOA is compared against the basic SOA, grey wolf optimizer(GWO), and improved SOA(ISOA) by using eight complex benchmark function optimization problems. Experimental results reveal that the proposed SFSOA has higher solution accuracy, faster convergence rate and stronger global search ability than its competitors and can effectively deal with complex function optimization problems. Finally, SFSOA is applied to solve the feature selection problems and the satisfactory results are obtained. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of aquila optimization algorithm（AO）, this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation; Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm; Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. Through the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem, The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum. [ABSTRACT FROM AUTHOR]

The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO) is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP), which is considered to be an NP-hard Combinatorial Optimization Problem (COP). A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO) algorithm on TSP were compared to other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Among population-based optimization algorithms guided by meta-heuristics, Particle Swarm Optimization (PSO) has gained significant popularity in the past two decades, particularly due to its ease of implementation and fast convergence capabilities. This paper seeks to translate the beneficial features of PSO from solving typical continuous single-objective problems to solving multi-objective mixed-discrete problems, which is relatively a new ground for PSO application. The original Mixed-Discrete PSO (MDPSO) algorithm, which included an exclusive diversity preservation technique to significantly mitigate premature particle clustering, has been shown to be a powerful single-objective solver for highly constrained MINLP problems. This papers makes fundamental advancements to MDPSO, enabling it to solve complex multi-objective problems with mixed-discrete design variables. Specifically, in the velocity update equation for any particle, the explorative term is modified to point towards a stochastically selected non-dominated solution at that iteration − thereby adopting the concept of multi-leader swarms. The fractional domain in the diversity preservation technique, which was previously defined in terms of the best global particle, is now formulated as a function of the extreme members in the set of intermediate Pareto optimal solutions. With this advancement, diversity preservation not only mitigates premature particle stagnation, but also promotes more uniform coverage of the Pareto frontier. The multi-objective MDPSO algorithm is tested using a set of benchmark problems and a wind farm layout optimization problem. To illustrate the competitive benefits of the new MO-MDPSO algorithm, the results are compared with those given by other popular multi-objective solvers such as NSGA-II and SPEA. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-objective uniform-diversity differential evolution (MUDE) algorithm is proposed and used for Pareto optimum design of mechanisms. The proposed algorithm uses a diversity preserving mechanism called the ε-elimination algorithm to improve the population diversity among the obtained Pareto front. The proposed algorithm is firstly tested on some constrained and unconstrained benchmarks proposed for the special session and competition on multi-objective optimizers held under IEEE CEC 2009. The inverted generational distance (IGD) measure is used to assess the performance of the algorithm. Secondly, the proposed algorithm has been used for multi-objective optimization of two different combinatorial case studies. The first case contains a two-degree of freedom leg mechanism with springs. Three conflicting objective functions that have been considered for Pareto optimization are namely, leg size, vertical actuating force, and the peak crank torque. The second case is a two-finger robot gripper mechanism with two conflicting objectives which are the difference between the maximum and minimum gripping force and the transmission ratio of actuated and experienced gripper forces. Comparisons of obtained Pareto fronts using the method of this work with those obtained in other references show significant improvements. [ABSTRACT FROM AUTHOR]

In this paper, multi-objective particle swarm optimization with preference information (MOPSO-PI) has been proposed. In the proposed algorithm, the information entropy is employed for measuring the probability distribution of particles; the user's preference information is represented as the ranking of each particle through the possible matrix. The optimal procedure is guided by the preference information since the global best performance of particle is randomly chosen among non-dominated solutions with higher ranking value in each iteration. Finally, the MOPSO-PI is applied to optimize the steelmaking process; the power supply curve obtained reduces the electric energy consumption, shortens the smelting time and prolongs the lifespan of the furnace lining. The application results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (P c) and mutation P m probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods. [ABSTRACT FROM AUTHOR]

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees. [ABSTRACT FROM AUTHOR]

In this study, a modified version of salp swarm algorithm (MSSA) is used to synthesize elliptical antenna arrays (EAAs). The original salp swarm algorithm (SSA) is an optimization algorithm inspired by the behavior of salps in nature, which is used to solve engineering problems. The main purpose of the synthesis in this study is to obtain an EAA pattern with low maximum sidelobe levels (MSLs) for a fixed narrow first null beamwidth (FNBW). For different examples, the amplitude and angular position values of the antenna array elements are considered as optimization parameters. To show the effectiveness of the MSSA, eight examples of EAAs with 8, 12, and 20 elements are given. The results obtained with MSSA are compared with those of the antlion optimization, symbiotic organizations search, flower pollination algorithm, and accelerated particle swarm optimization from the literature. It is clear from the numerical results that MSSA outperforms the other algorithms in terms of the suppression of MSL. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithm is a stochastic global optimization algorithm, but it is easy to fall into local optima and premature. In order to overcome its defects, a diversity driven adaptive particle swarm optimization（DDA-PSO） algorithm is proposed in this paper. The algorithm includes the attraction stage and the driving stage. In the attraction stage, the linear decreasing inertia weight mechanism is used to accelerate the particle convergence, and in the driving stage, the diversity driving speed strategy is used to improve the population diversity. The two stages adaptively switch to each other, the particles can jump out of the local optima and prevent premature, the algorithm obtains an adaptive balance between the exploration and exploitation. The experimental results show that DDA-PSO algorithm improves the convergence speed and accuracy, and the global search ability is significantly improved. [ABSTRACT FROM AUTHOR]

Aiming at the problem of "over-exploitation" in dimensional learning strategy (DLS), this paper proposed a multiswarm collaborative particle swarm optimization algorithm with comprehensive dimension learning (CDL-MCPSO). The cluster structure of master-slave paradigm divides the population into a master group and four slave groups, in which the master group executes a comprehensive learning strategy to conduct a large-scale exploration in the search space, and the slave groups execute a comprehensive dimensional learning strategy (CDL) to exploit high-precision solutions near the local optimal. The master-slave groups can effectively achieve the balance between exploration and exploitation by executing algorithms with different functions. At the same time, in order to maintain the diversity of the population, the CDL-MCPSO proposed a new solution exchange mechanism (SEM), By doing this, the algorithm can accomplish information exchange and cooperation after the master-slave groups running their respective algorithms for several generations independently, so as to guide the particles to conduct more accurate searches in the later stage. Finally, for the high randomness of the initialization process, the algorithm adopted latin hypercube sampling to reconstruct the input distribution. In order to verify the effectiveness of CDL-MCPSO, compared with 5 kinds of PSO variants in 10 test functions. the results show that the algorithm can always find better or equivalent solutions. It is feasible and efficient in solving complex functions. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm and optimization procedure for the optimization of the outer rotor structure of the brushless DC (BLDC) motor. The optimization software was developed in the Delphi Tiburón development environment. The optimization procedure is based on the salp swarm algorithm. The effectiveness of the developed optimization procedurewas compared with genetic algorithm and particle swarmoptimization algorithm. The mathematical model of the device includes the electromagnetic field equations taking into account the non-linearity of the ferromagnetic material, equations of external supply circuits and equations of mechanical motion. The external penalty function was introduced into the optimization algorithm to take into account the non-linear constraint function. [ABSTRACT FROM AUTHOR]

In unknown environments, multiple-robot cooperation for target searching is a hot and difficult issue. Swarm intelligence algorithms, such as Particle Swarm Optimization (PSO) and Fruit Fly Optimization Algorithm (FOA), are widely used. To overcome local optima and enhance swarm diversity, this paper presents a novel multi-swarm hybrid FOA-PSO (MFPSO) algorithm for robot target searching. The main contributions of the proposed method are as follows. (1) The improved FOA (IFOA) provides a better value for the improved PSO (IPSO) to find the next optimal robot position value. (2) Multi-swarm strategy is introduced to enhance the diversity and achieve an effective exploration to avoid premature convergence and falling into local optima. (3) An escape mechanism named MSCM (Multi-Scale Cooperative Mutation) is used to address the limitation of local optima and enhance the escape ability for obstacle avoidance. All of the aspects mentioned above lead robots to the target without falling into local optima and allow the search mission to be performed more quickly. Several experiments in four parts are performed to verify the better performance of MFPSO. The experimental results show that the performance of MFPSO is much more significant than that of other current approaches. [ABSTRACT FROM AUTHOR]

The quality of service multicast routing problem is a very important research issue for transmission in wireless mesh networks. It is known to be NP-complete problem, so many heuristic algorithms have been employed for solving the multicast routing problem. This paper proposes a modified binary bat algorithm applied to solve the QoS multicast routing problem for wireless mesh network which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate to get low-cost multicasting tree. The binary bat algorithm has been modified by introducing the inertia weight w in the velocity update equation, and then the chaotic map, uniform distribution and gaussian distribution are used for choosing the right value of w. The aim of these modifications is to improve the effectiveness and robustness of the binary bat algorithm. The simulation results reveal the successfulness, effectiveness and efficiency of the proposed algorithms compared with other algorithms such as genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization algorithm, bacteria foraging-particle swarm optimization, bi-velocity discrete particle swarm optimization and binary bat algorithm. [ABSTRACT FROM AUTHOR]

Motion planning problem though widely studied in robotics is a difficult problem. It finds a feasible path from an initial position to a final position in an environment with obstacles. Recent researches do not just aim to find feasible paths but to find paths that are optimal in respect to time, distance and safety of the robots. Optimization based techniques have been proposed to solve this problem but some of them used techniques that may converge to local minimum and they seldom consider the speed of the technique. Hence this paper presents a fast and global motion planning algorithm for a mobile robot in a known environment with static obstacles. It uses particle swarm optimization (PSO) technique for convergence to global minimum and a customized algorithm which generates the coordinates of the search space. The coordinate values when generated by the customized algorithm are passed to the PSO algorithm which uses them to determine the shortest path between the two given end positions. We perform our experiments using four different environments with population sizes 100, 50, 20 and 10 in a 10 × 10 grid and our results are favorable. [ABSTRACT FROM AUTHOR]

This paper considers an optimal advertising strategy problem. This is an important problem in marketing investment for new products in a free market. The main contributions of this paper are as follows. First, the problem is formulated as an optimal control problem of switched impulsive systems with the state inequality constraints, which is different from the existing nonlinear system models. As the complexity of such constraints and the switching instants are unknown, it is difficult to solve this problem by using conventional optimization techniques. To overcome this difficulty, by applying the penalty function, all the state inequality constraints are first written as non-differentiable penalty terms and imposed into the cost function. Then, the penalty terms are smoothed by using a novel smooth function, leading to a smooth optimal control problem with no state inequality constraints, and an improved gradient-based particle swarm optimization (PSO) algorithm is proposed for solving this problem. Error analysis results show that if the adjustable parameter is sufficiently small, the solution of the smooth optimal control problem is approximately equal to the original problem. Finally, a switched impulsive system for beer sales is established to illustrate the effectiveness of the developed algorithm. [ABSTRACT FROM AUTHOR]

Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem. [ABSTRACT FROM AUTHOR]

An R2 indicator-based multi-objective particle swarm optimiser (R2-MOPSO) can obtain well-convergence and well-distributed solutions while solving two and three objectives optimisation problems. However, R2-MOPSO faces difficulty to tackle many-objective optimisation problems because balancing convergence and diversity is a key issue in high-dimensional objective space. In order to address this issue, this paper proposes a novel algorithm, named R2-MaPSO, which combines the R2 indicator and decomposition-based archiving pruning strategy into particle swarm optimiser for many-objective optimisation problems. The innovations of the proposed algorithm mainly contains three crucial factors: (1) A bi-level archiving maintenance approach based on the R2 indicator and objective space decomposition strategy is designed to balance convergence and diversity. (2) The global-best leader selection is based on the R2 indicator and the personal-best leader selection is based on the Pareto dominance. Meanwhile, the objective space decomposition leader selection adopts the feedback information from the bi-level archive. (3) A new velocity updated method is modified to enhance the exploration and exploitation ability. In addition, an elitist learning strategy and a smart Gaussian learning strategy are embedded into R2-MaPSO to help the algorithm jump out of the local optimal front. The performance of the proposed algorithm is validated and compared with some algorithms on a number of unconstraint benchmark problems, i.e. DTLZ1-DTLZ4, WFG test suites from 3 to 15 objectives. Experimental results have demonstrated a better performance of the proposed algorithm compared with several multi-objective particle swarm optimisers and multi-objective evolutionary algorithms for many-objective optimisation problems. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization algorithm based on Particle Swarm Optimization (PSO) is proposed for solving the non-convex non-linear economic dispatch (ED) problem in a system integrated with combined heat and power. In the proposed method, which is known as Advanced Modified PSO (AMPSO), one third of the population is updated based on conventional PSO, and the remaining members are updated randomly based on the boundaries of variables. In order to improve the efficiency of the algorithm, the required coefficients were estimated by the Taguchi method. The optimization problem is the nonlinear non-convex ED in which the effects of steam valves and the power loss have been included. The proposed algorithm is applied on different systems, and the results are compared with those of some well-regarded methods developed by other researchers. The results show the superiority of the proposed AMPSO in comparison to other methods. [ABSTRACT FROM AUTHOR]

Real-time drilling optimization is a topic of significant interest because of its economic value, and its importance increases particularly during periods of low oil prices. This paper evaluates different optimization strategies and algorithms for real-time optimization of an objective function (function to be optimized) specific to drilling. The objective function optimized here is derived from a data-driven (or machine-learning) model with an unknown functional form. A data-driven model has been used to calculate the objective function [rate of penetration (ROP)] because it has been shown to be more efficient in ROP prediction relative to deterministic models (Hegde and Gray 2017). The data-driven ROP model is built using machine-learning algorithms; measured drilling parameters [weight on bit (WOB), revolutions per minute (rev/min), strength of rock, and flow rate] are used as inputs to predict the ROP. Real-time drilling optimization that is data-driven is challenging because of run-time constraints. This is perceived as a handicap for data-driven models because their functional form is unknown, making them more difficult to optimize. This paper evaluates algorithms depending on their ability to best maximize the objective (ROP) and their time effectiveness. Two simple yet robust algorithms, the eyeball method and the random-search method, are presented as plausible solutions to this problem. These methods are then compared with popular metaheuristic algorithms, evaluating the tradeoff between improvement in the objective (search for a global optimal) and the computational time of run. Using results from the simulations conducted in this paper, we concluded that data-driven models can be used for real-time drilling despite their computational constraints by choosing the right optimization algorithm. The best tradeoff in terms of ROP increase as well as computational efficiency evaluated in this paper is the simplex algorithm. The ROP was improved by 30% on average with a variance of 2.5% in the test set over 14 formations that were tested. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based stochastic algorithm modeled on the social behaviors observed in flocking birds. Over the past quarter century, the particle swarm optimization algorithm has attracted many researchers’ attention. Through the convergent operation and divergent operation, individuals in PSO group and diverge in the search space/objective space. In this paper, the historical development, the state-of-the-art, and the applications of the PSO algorithms are reviewed. In addition, the characteristics and issues of the PSO algorithm are also discussed from the evolution and learning perspectives. Every individual in the PSO algorithm learns from itself and another particle with good fitness value. The search performance and convergence speed were affected by different learning strategies. The scheduling and data-mining problems are illustrated as two typical cases of PSO algorithm solving real-world application problems. With the analysis of different evolution and learning strategies, particle swarm optimization algorithm could be utilized on solving more real-world application problems effectively, and the strength and limitation of various PSO algorithms could be revealed. [ABSTRACT FROM AUTHOR]

Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance. [ABSTRACT FROM AUTHOR]

An accurate and efficient control of the Maximum Power Point Tracking (MPPT) and pitch angle for the Wind Turbine (WT) system can provide more energy while also protecting the material. Thus, considering these economic aspects, many controllers are proposed to guarantees their performances. The most studied strategy is the classical PID controller. However, its performance is limited due to the nonlinear model and the difficult wind speed form, which inflict many uncertainties. To solve these drawbacks, an advanced controller named Type-2 Fractional Order Fuzzy PID (T2-FOPID) controller is suggested in this paper to improve the exigence controls. Meanwhile, an efficient optimization technique named the Salps Swarm Algorithm (SSA) has been introduced in order obtaining the best controller parameters. The efficiency of the proposed controller is tested to the 10 kW-WT under various wind speed scenarios. The derived results explicitly indicate that the proposed strategy outperforms the PID controller and other controllers, in terms. the minimum of error, the overshoot and the settling time. • The pricipal controls of the wind turbine system are are the MPPT and the pitch angle. • The authers proposed the Type-2 Fractional order PID controller based on salps swarm algorithm. • The results proved the superiority of proposed controller than the classical PID controller and others are largely suggested in the literature. [ABSTRACT FROM AUTHOR]

The optimization performance of the PSO algorithm is affected by the speed update formula. Too fast convergence speed may cause the algorithm to miss the global optimal solution; too slow convergence speed may cause the algorithm to fall into the local optimal solution. Aiming at this problem, this paper proposes a PSO optimization algorithm based on improved compression factor, namely FPSO. By introducing the compression factor equation, the speed iteration formula is improved, and the influence on the algorithm caused by the improper setting of the learning factor is reduced. The new adjustment mechanism not only ensures the convergence performance of the PSO algorithm, but also weakens the influence of the speed boundary on the algorithm. Finally, five classical functions are selected to test the performance of the algorithm. The test results show that, compared with the traditional PSO algorithm, the proposed algorithm improves the global convergence ability and shortens the convergence time. [ABSTRACT FROM AUTHOR]

Aiming at the problem of blank and overlapping coverage in node deployment of wireless sensor network, this paper proposed a network coverage optimization based on improved equilibrium optimizer (IEO) algorithm. Firstly, it used surround reverse learning to improve the quality of initial population and enhance the optimization ability of the algorithm. Secondly, it introduced dynamic sine and cosine factors to further balance global search and local development ability, so that particle population could search extensively and dig deeply in the search space. Finally, it added circle chaos map in the concentration updating stage to increase the diversity of population and improve the algorithm's ability to escape local optimum. The experimental results show that the IEO algorithm applied to the coverage optimization experiment of WSN can effectively reduce the deployment cost, show higher network coverage and improve the monitoring quality of the network compared with the standard balance optimizer algorithm and other improved algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present a parallel hybrid metaheuristic that combines the strengths of the particle swarm optimization (PSO) and the genetic algorithm (GA) to produce an improved path-planner algorithm for fixed wing unmanned aerial vehicles (UAVs). The proposed solution uses a multi-objective cost function we developed and generates in real-time feasible and quasi-optimal trajectories in complex 3D environments. Our parallel hybrid algorithm simulates multiple GA populations and PSO swarms in parallel while allowing migration of solutions. This collaboration between the GA and the PSO leads to an algorithm that exhibits the strengths of both optimization methods and produces superior solutions. Moreover, by using the "single-program, multiple-data" parallel programming paradigm, we maximize the use of today's multicore CPU and significantly reduce the execution time of the parallel program compared to a sequential implementation. We observed a quasi-linear speedup of 10.7 times faster on a 12-core shared memory system resulting in an execution time of 5 s which allows in-flight planning. Finally, we show with statistical significance that our parallel hybrid algorithm produces superior trajectories to the parallel GA or the parallel PSO we previously developed. [ABSTRACT FROM AUTHOR]

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This paper mainly discusses the application of the particle swarm optimization in logistics distribution routing problems. Combining with the characteristics of logistics and distribution, it established a mathematical model of the distribution routing problem. Introducing three kinds of optimization strategies in the particle swarm optimization to optimize the particle swarm algorithm, constructing three different particle swarm algorithms of LinWPSO, SAPSO and RandWPSO, used respectively the standard of PSO, LinWPSO, SAPSO and RandWPSO to solve calculation cases of problems in logistics and route, the results showed that the performance of the LinWPSO, SAPSO and RandWPSO to solve vehicle routing problem is better than standard PSO. Performance of SAPSO is optimal, which can effectively solve vehicle routing problems of the logistics distribution, when the problem size increases, optimization advantages of the SAPSO will display fully, we can greatly shorten the delivery mileage by using the SAPSO to solve the logistics distribution routing problem. [ABSTRACT FROM AUTHOR]