Self-adaptive Particle Swarm Optimization Research Articles

Integrated dynamic evaluation of depletion-drive performance in naturally fractured-vuggy carbonate reservoirs using DPSO–FCM clustering

Compared to the widely distributed porous and fractured-porous carbonate reservoirs, the naturally fractured-vuggy carbonate reservoirs (NFVCRs) found in Tarim Basin, China, suffer from strong heterogeneity, multiple types of reservoir bodies, poor connectivity, complicated flow behavior and various water–oil interplay relationship in most cases. Due to lack of powerful and suitable EOR (enhanced oil recovery) strategies, depletion-drive recovery is commonly adopted for practical production, and the efficient oilfield development remains a great challenge to all of us. Understanding the relationship between reservoir bodies, configuration, water–oil interplay and depletion-drive performance, is essential to exchange of oilfield development scheme and further potential tapping of the remaining oil. By introducing an self-adaptive particle swarm optimization (DPSO) to tackle the shortcomings of the conventional fuzzy c-means (FCM) in terms of sensitivity to initial values, initialization with randomly generated clustering centers, and easy involvement in local minima, a novel integrated dynamic evaluation method for depletion-drive performance in NFVCRs based on DPSO–FCM clustering is developed. In this paper, different fractured-vuggy reservoir types are identified combing drilling & well logging response, numerical well test analysis and production test. The integrated diagnosis techniques including rate transient analysis, production decline analysis and reservoir energy evaluation, are employed to evaluate reservoir properties, furthermore, principal component analysis (PCA) is performed to reduce the dimensionality of input vector and establish the dynamic evaluation index system of depletion-drive performance. Taking a typical NFVCR in northern Tarim Basin as example, the proposed method is implemented to classify the production responses into four major patterns with eight sub-classes. Moreover, according to the insight of water–oil interplay relationship, depletion-drive production responses and identification of reservoir types, the water influx behaviors are divided into four general patterns. Eventually, the relevant dynamic clustering criterion and suitable potential-tapping strategies or measures are provided.

A generalized Prandtl-Ishlinskii model for characterizing the rate-independent and rate-dependent hysteresis of piezoelectric actuators.

In this paper, a generalized hysteresis model is developed to describe both rate-independent and rate-dependent hysteresis in piezoelectric actuators. Based on the classical Prandtl-Ishlinskii (P-I) model, the developed model adds a quadratic polynomial and makes other small changes. When it is used to describe rate-independent hysteresis, the parameters of the model are constants, which can be identified by self-adaptive particle swarm optimization. The effectiveness of this rate-independent modified P-I model is demonstrated by comparing simulation results of the developed model and the classic Prandtl-Ishlinskii model. Simulation results suggest that the rate-independent modified P-I model can describe hysteresis more precisely. Compared with the classical P-I model, the rate-independent modified P-I model reduces modeling error by more than 50%. When it is used to describe rate-independent hysteresis, a one-side operator is adopted and the parameters are functions with input frequency. The results of the experiments and simulations have shown that the proposed models can accurately describe both rate-independent and rate-dependent hysteresis in piezoelectric actuators.

A review of image reconstruction methods in electrical capacitance tomography

In this paper, we review image reconstruction methods and their suitability in electrical capacitance tomography measurement system. These methods can be grouped into direct and iterative methods. Direct methods include Linear back projection, Singular value decomposition, and Tikhonov regularization. Iterative methods are further divided into algebraic and optimization methods. Algebraic reconstruction methods include iterative linear back projection, iterative Tikhonov, Landweber iteration, simultaneously algebraic reconstruction, and model$ - $based reconstruction. Optimization methods include fuzzy mathematical modeling, genetic algorithms, artificial neural networks, generalized vector sampled pattern matching, total variation regularization, regularized total least squares, extended Tikhonov regularization, simulated annealing, compressed sensing principle, population entropy, adaptive differential evolution, least$ - $squares support vector machine, and self-adaptive particle swarm optimization. Some of these methods have been examined through experiments and their comparative analysis have been given. Results show that iterative methods generate high quality images compared with non-iterative ones when evaluated over full component fraction range. However, iterative methods are computationally expensive, and hence used for research and off-line investigations rather than for on-line process monitoring.

A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithm

A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithm

A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithm

When used to deal with complex functions with high dimension, Bacterial Foraging Algorithm BFA converges slowly and Particle Swarm Optimisation PSO algorithm tends to premature convergence and low accuracy. Aiming at these shortcomings, an improved hybrid optimisation algorithm based on PSO and BFA is proposed in the paper ABSO for short. The ABSO algorithm adds extremum disturbance to PSO. It also adaptively improves learning factors and inertial weight of PSO, chemotaxis step-length and disperse probability of BFA, respectively. BFA is used as the whole frame of the hybrid algorithm. After the chemotaxis operation of BFA, PSO is introduced to help BFA escape from local optima. This combines organically the optimisation update mechanism of PSO and the chemotaxis update mechanism of BFA, and can well balance the global search and local development capabilities. Simulation results on four benchmark functions show that the ABSO algorithm is superior to BFA, PSO, self-adaptive PSO and two other kinds of BFA hybrid algorithm in convergence speed, accuracy and robustness. This proves the validity of the ABSO algorithm in high-dimensional function optimisation problems.

Probabilistic evaluation for static voltage stability for unbalanced three‐phase distribution system

To investigate the impact of the randomness of renewable energy generation on distributed network, the authors propose a non-linear three-phase maximum loadability model. Considering uncertainty such as wind power generation and load forecasting deviation, a probabilistic evaluation of static voltage stability for distributed network is presented based on cumulants method. Correlations between adjacent wind farms are also considered. Maximal load increment models of three-phase balanced/unbalanced power system are solved by improved self-adaptive particle swarm optimisation algorithm. Two approximation expansions named Cornish–Fisher expansion and Gram–Charlier expansion are employed to obtain probability distribution of random variables. Relation of probabilistic performances including means and variances between balanced system and unbalanced system could be obtained. 25-bus/33-bus unbalanced system and 33-bus balanced system are studied as examples to validate effectiveness of their proposed method, and results based on cumulants method are compared with Monte Carlo simulations.

A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks

With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective function which is used to estimate the location of sources. Therefore, the multiple-source localization problem is transformed into optimization problem. In order to improve the ability of global search of particle swarm optimization, the self-adaptive particle swarm optimization is used to solve this problem. Various simulations have been conducted, and the results show that the proposed method owns higher localization accuracy in comparison with other methods.

Self-adapting hybrid strategy particle swarm optimization algorithm

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

Novel self-adaptive particle swarm optimization methods

This paper proposes adaptive versions of the particle swarm optimization algorithm (PSO). These new algorithms present self-adaptive inertia weight and time-varying adaptive swarm topology techniques. The objective of these new approaches is to avoid premature convergence by executing the exploration and exploitation stages simultaneously. Although proposed PSOs are fundamentally based on commonly utilized swarm behaviors of swarming creatures, the novelty is that the whole swarm may divide into many sub-swarms in order to find a good source of food or to flee from predators. This behavior allows the particles to disperse through the search space (diversification) and the sub-swarm, where the worst performance dies out while that with the best performance grows by producing offspring. The tendency of an individual particle to avoid collision with other particles by means of simple neighborhood rules is retained in these algorithms. Numerical experiments show that the new approaches, survival sub-swarms adaptive PSO (SSS-APSO) and survival sub-swarms adaptive PSO with velocity-line bouncing (SSS-APSO-vb), outperform other competitive algorithms by providing the best solutions on a suite of standard test problem with a much higher consistency than the algorithms compared.

Dynamically Self-Adaptive Fuzzy PSO Technique for Smart Diagnosis of Transverse Crack

An artificial intelligence (AI)-based methodology is proposed for the damage diagnosis of beam members of engineering structures under complex loading conditions. The current proposed method has been developed using particle swarm optimization (PSO) technique. A single transverse open-edge crack has been developed on a beam structure, modeled by a local flexibility matrix that is calculated analytically to determine natural frequencies and mode shapes. The modified PSO employs the strategy of nonlinearly decreasing inertia weight, which varies from a large value to a small value. Furthermore, a fuzzy adaptive PSO (APSO) has been used that incorporates the dynamically varying inertia weight based on the variance of the population fitness. Numerical and experimental studies on the cracked beam structure were also conducted to ensure the integrity of the above algorithms. The results show that both the size and location of the crack can be predicted efficiently through the proposed APSO.

Solving multi-mode time–cost–quality trade-off problems under generalized precedence relations

In this paper, we model a multi-mode time–cost–quality trade-off project scheduling problem under generalized precedence relations using mixed-integer mathematical programming. Several solution procedures, including the classical epsilon-constraint, the efficient epsilon-constraint method, dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO), and the multi-start partial bound enumeration algorithm, are provided to solve the proposed model. Several test problems are simulated and solved with the four methods and the performance of the methods are compared according to a set of accuracy and diversity comparison metrics. Additional analyses and tests are performed on the generated Pareto fronts of the solution procedures. Computational experiments are conducted to determine the validity and the efficiency of the DSAMOPSO method. The results show that this method outperforms the other three methods. We also carry out a sensitivity analysis of the DSAMOPSO algorithm to study the effects of parameter changes on the CPU time.

Solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self-adaptive particle swarm optimization and K-harmonic means

Excellent prediction modeling of CO2solubility in polymers using hybrid computation algorithm.

A Self-adaptive Global Particle Swarm Optimization Algorithm for Unconstrained Optimization Problems

This paper aims to present a self-adaptive global particle swarm optimization (SGPSO) algorithm for solving unconstrained optimization problems. In the new algorithm, the inertia weights are generated based on Gaussian distribution, which is helpful to improve the diversity of the population. In addition, the worst particle is updated by averaging the other particles, which is beneficial to improving the quality of the population. Finally, a global disturbance is adopted to increase the convergence rate of SGPSO. In the disturbance process, a disturbance factor is utilized to control the searching ranges of the population, which can effectively keep a balance between the global exploration and local exploitation. Twenty well-known benchmark functions are considered to evaluate the performance of SGPSO, and 50 runs are implemented in each case. Numerical experiments and comparisons demonstrate that SGPSO is superior to the other three algorithms according to means, standard deviations and convergence rate.

A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction

The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO–GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the RBF by GA operation and the parameters of the basis and weights by a PSO–GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO–GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models; and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7–11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85billionkWh. The lowest demand is estimated for the business-as-usual scenario, and will be 88.45billionkWh.

Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique

This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non-convex emission constrained economic dispatch (NECED) problems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for non-convex emission constrained economic dispatch (NECED) problems to avoid premature convergence. Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation. Non-convex emission constrained economic dispatch (NECED) problem is obtained by considering both the economy and emission objectives. The performance of the proposed method is demonstrated on two sample test systems. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses.

Optimal and secure audio watermarking scheme based on self-adaptive particle swarm optimization and quaternion wavelet transform

In this paper, a new audio watermarking scheme based on self-adaptive particle swarm optimization (SAPSO) and quaternion wavelet transform (QWT) is proposed. By obtaining optimal watermark strength using a uniquely designed objective function, SAPSO addresses the conflicting problem of robustness, imperceptibility, and capacity of audio watermarking scheme using self-adjusted parameters. To withstand de-synchronization attack, a synchronization sequence generated by chaotic signals is also adopted in our scheme. Furthermore, the utilization of chaotic signals significantly enhances the security of the proposed scheme. The experimental results validate that our scheme is not only robust against de-synchronization attack, but also typical signal manipulations and StirMark attack. Our comparative analysis also revealed that the proposed scheme outperforms the state-of-the-arts audio watermarking schemes.

A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times

This paper presents mathematical modeling of joint lot sizing and scheduling problem in a job shop environment under a set of realistic working conditions. One main realistic assumption of the current study is dealing with flexible machines able to change their working speeds, known as process compressibility. The production schedules should be subject to limited available time in every planning period. Also, the model assumes that periodical sequences should be determined in a way that they obey from a fixed global sequence. Another complicating aspect of the problem is about consideration of precedence relationships for the needed processes of an item type over the corresponding machines. As the problem is proved to be strongly NP-hard, it is solved by a particle swarm optimization (PSO) algorithm. The proposed algorithm is self-controller about its working parameters, time to stop the search process, search diversification/intensification, and totally about its behavior. Performance of the algorithm is verified in terms of optimality gap using Lingo 11.0 on a set of randomly generated test data.

Self-adaptive particle swarm optimization with multiple velocity strategies and its application for p-Xylene oxidation reaction process optimization

Particle swarm optimization (PSO) has been successful in solving many benchmark test functions and real-world industrial problems over the past decades. However, the performance of PSO is significantly affected by the choice of control parameters and the design of velocity updating strategies. Therefore, a self-adaptive PSO with multiple velocity strategies (SAPSO-MVS) is proposed to improve PSO performance. SAPSO-MVS can generate self-adaptive control parameters during the entire evolution process and use a new velocity updating strategy. To test the effectiveness of the proposed algorithm, SAPSO-MVS is compared with 8 well-known state-of-the-art PSO variants and 3 famous non-PSO algorithms on a set of benchmark test functions. Simulation results show that the average performance of the proposed algorithm is better than the performances of other compared algorithms. SAPSO-MVS is also used to optimize the 10 operation conditions of the p-Xylene oxidation reaction process. Satisfactory results are obtained.

A new sense-through-foliage target recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine

Sense-through-foliage target detection and recognition is of interest to both military and civilian research. In this paper, a new recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine (SVM) is proposed to recognize targets obscured by foliage. To seek the optimal parameters of SVM, a new hybrid differential evolution and self-adaptive particle swarm optimization (DEPSO) algorithm is developed to determine the optimal parameters for SVM with the highest accuracy and generalization ability. In this work, sparse representation is applied to extract the target features from real target echo waveforms measured by a bistatic ultra-wideband (UWB) radar system. Then, the extracted features are input into the proposed method to automatically recognize the types of targets. This method is validated by experiments taken in the forest environment. Compared with the commonly used particle swarm optimization-optimized SVM (PSO-SVM), SVM, k-nearest neighbor (KNN) and BP neural network (BPNN), the proposed DEPSO-SVM can achieve a higher accuracy. Experimental results demonstrate the effectiveness and robustness of the proposed method for sense-through-foliage target recognition.

Self-adaptive Particle Swarm Optimization Algorithm based on Directed-weighted Complex Networks

In the process of the optimization iteration in high-dimensional space, standard particle swarm optimization algorithm falls into local optimum easily and its convergence speed is also very slow. In order to solve these problems, a self-adaptive particle swarm optimization based on the directed-weighted complex networks is proposed by introducing the complex networks. In our algorithm, we use the small-scope network with directed links to initialize the particle swarm topological structure and introduce the evolution mechanism of directed dynamic networks, which will make the particle topological structure is evolved in scale-free networks when the in-degree is complied with the power-law distribution. Therefore, our proposed algorithm will improve the learning diversity among these particles, and avoid these particles fall into local optimum easily. The simulation experiment demonstrates that the proposed algorithm will improve the premature convergence problem and enhance the algorithm convergence speed.