Self-organizing Hierarchical Particle Swarm Optimization Research Articles

Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion

This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the b parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (qu) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (DR) significantly affects the variation in the bearing capacity factor (Nγ) at higher Q values, while lower Q values inhibit dilatancy due to soil crushing. The width of the strip footing (B) has a decreasing effect on Nγ at higher Q values, and the unit weight (γ) changes minimally impact Nγ within the range of 16–22 kN/m3. Additionally, an increase in the critical state friction angle (ϕcv) consistently increases Nγ, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (Q) as the most significant factor influencing the bearing capacity factor (Nγ).

Applying Particle Swarm Optimization for Investigating the Stability of DC-DC Buck-Boost Converter: A Software Based Approach

ABSTRACT Stability of power converters has always been a major concern when dealing with nonlinear behavior of the system and swarm-based optimization techniques are proven to be handy as they are widely employed. Hence, this study presents an investigative approach to explore the compatibility of applying different variants of Particle Swarm Optimization (PSO) to design an optimized PID controller for DC-DC Buck-Boost converter. This work assesses converter stability through optimization of the PID controller using classical PSO (cPSO), Time-Varying Inertia Weight PSO (TVIW-PSO), Chaotic Descending Inertia Weight PSO (CDIW-PSO), Self-organizing Hierarchical PSO (HPSO) and Random inertia weight PSO (RIW-PSO). The State Space Average (SSA) method is utilized to model and attain the transfer function; eigenvalue analysis and time domain simulation analysis are considered to investigate the stability. Moreover, four objective functions and various performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated to assess the system’s efficiency and the outcomes of different approaches are compared extensively. Amongst all, CDIW-PSO showcased better performances e.g. overshoot of 1.08%, settling time 0.000228s and rise time 0.000143s. The findings illustrate algorithm’s capacity to reduce transient and steady-state faults and makes it compatible with more nonlinear applications.

Optimum dynamic dispatch of clean water, methane, electricity and heat considering carbon capture

This paper suggests a novel clean water, natural gas, heat and power generation system using wind turbine generators, PVT panels, bioenergy power plants, micro-hydro power plants, micro-cogeneration units (MCUs), battery energy storage system, thermal energy storage system and plug-in electric vehicles (PEVs). In this system, a power to gas technology consisting of a carbon capture unit, an electrolyzer, a hydrogen storage unit, and a methanation process, is used to supply the natural gas demand using CO2 captured from the MCUs and the hydrogen generated by the clean water electrolyzer. Furthermore, the electricity is consumed by a desalination unit to procure the clean water demand, carbon captured unit (CCU), electrolyzer water inflow, and fill up the water storage tank. At peak clean water demand periods, the excess water is pumped from the storage tank to the consumption side. Demand response program (DRP) is used for levelling the demand. Here, total cost for water-gas-heat-power nexus model throughout the day with and without DRP for three different scenarios is minimized by using quasi-oppositional fast convergence evolutionary programming, fast convergence evolutionary programming, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and differential evolution. It is seen from numerical results that cost attained with DRP is less than the cost attained without DRP.

Scenario-based fuel-constrained heat and power scheduling of a remote microgrid

This manuscript suggests elephant clan optimization (ECO) algorithm to solve heat and electric power scheduling of a remote microgrid (MG) for three different scenarios considering fuel constraints. ECO algorithm is a populace-based method motivated by the elephants’ behaviour and their societal organization. MG comprises diesel generators (DGs), small hydro power plants (SHPPs), wind turbine generators (WTGs), solar micro-cogeneration (SMC) unit, biomass-fuel-fired micro-cogeneration (BMC) unit, battery energy storage system (BESS), plug-in electrical vehicles (PEVs) and thermal energy storage system (TESS). BMC units and SMC units are incorporated alternately into the MG. Numerical results of a typical system are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). It is seen from the evaluation that ECO offers superior solution.

Dynamic optimal power flow for isolated microgrid incorporating renewable energy sources

Enhanced investiture to distribution generation (DG) had moved for alteration of existing networks for developing microgrid which can function autonomously or together with grid. Dynamic optimal power flow (DOPF) for isolated microgrid incorporating renewable energy sources has been studied here. The DOPF problem has been solved by using elephant clan optimization (ECO) algorithm, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). The IEEE 33-bus system, IEEE 69-bus system, IEEE 118-bus system and real existing isolated microgrid of 15-bus system have been used for validation. In all the four test systems, the cost obtained from ECO is the lowest among all the three techniques.

Improved Particle Swarm Optimization Screening Iterative Algorithm in Gravity Matching Navigation

The promotion of matching precision is one of the key technical problems of gravity-assisted inertial navigation. In this study, first, the viability of the particle swarm optimization (PSO) algorithm applied to gravity matching inertial navigation is analyzed, and the original PSO algorithm is improved. The similarity of single-point matching is used to round off the matching result points, and the difference between the matching result coordinates and the corresponding inertial coordinates is calculated over a certain period to filter out the relatively poorer matching points. Finally, by calculating the coordinate distance of the first and last points between the consecutive valid matching segments and setting the limit difference, the coordinates of the invalid points between two adjacent valid matching points that meet the limit difference are projected to realize the correction of matching points. The test results show that the improved PSO-based screening iterative gravity matching algorithm can avoid the additional matching error caused by the setting of the search resolution compared with the PSO, new self-organizing hierarchical PSO (NHPSO), and adaptive PSO (APSO) matching algorithm and has significant improvement in the matching accuracy.

Fuel constrained combined heat and power dynamic dispatch using horse herd optimization algorithm

Due to gradually reduction of fossil fuel, the cost-effective use of available fuel for electric power generation has turn out to be a very significant concern of electric power utilities. This work recommends horse herd optimization algorithm (HOA) to solve fuel constrained combined heat and power dynamic economic dispatch with demand side management integrating wind turbine generators, solar PV plants and pumped storage hydro plant for three different scenarios. The effectiveness of the recommended method is divulged on an archetypal system. Numerical results of archetypal system for three different scenarios are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming, differential evolution and real-coded genetic algorithm. It has been seen from numerical results, that the total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the recommended HOA has the capability to confer with superior-quality solution.

Horse Herd Optimization Algorithm for Fuel Constrained Day-ahead Scheduling of Isolated Nanogrid

ABSTRACT Day-ahead scheduling of isolated nanogrid (NG) is an important task in power system. Owing to slowly lessening of fossil fuel, the profitable use of available fuel for electric power generation has turn out to be an extremely important concern of electric power utilities. This paper suggests horse herd optimization algorithm (HOA) to solve fuel constrained day-ahead scheduling of isolated nanogrid (NG) for five neighboring homes. NG comprises diesel generators, solar PV plants, battery energy storage system (BESS) and plug-in electric vehicles (PEVs). Simulation results of the test system have been compared with those obtained from social group entropy optimization, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution. It has been observed from the comparison that the suggested HOA has the capacity to give better solution.

Social group entropy optimization for day-ahead heat and power scheduling of an isolated microgrid

This article presents day-in-advance microgrid heat and electric power scheduling with and without demand-side management. The objective is to minimize the cost of heat and electric power generation, considering solar and wind power uncertainty. This scheduling problem is solved using a social group entropy optimization (SGEO) technique. The proposed microgrid comprises diesel generators, biomass-fuel-fired combined heat and power (BCHP) units, mini-hydro power plants, solar photovoltaic plants, wind turbine generators (WTGs), a hydrogen storage system (HSS) and plug-in electric vehicles (PEVs). A system with three diesel generators, three BCHP units, one mini-hydro power plant, two WTGs, two solar photovoltaic plants, one HSS and PEVs is studied to evaluate the suggested SGEO technique. Test results of the proposed SGEO technique are compared with those from self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution, and the recommended SGEO is shown to produce superior results.

Fuel constrained dynamic economic dispatch with demand side management

Owing to slowly reduction of fossil fuel, the cost-effective use of available fuel for electric power generation has turn out to be a very important concern of electric power utilities. Thermal power plants have to operate within their fuel confines and contractual constraints. This work recommends social group entropy optimization (SGEO) technique to solve fuel constrained dynamic economic dispatch (FCDED) with demand side management (DSM) integrating renewable energy sources and pumped hydro storage plant. Here the dynamic economic dispatch (DED) problem is solved with and without fuel constrained. The effectiveness of the recommended technique is revealed on two test systems. Simulation results of two test systems have been compared with those acquired from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE). Test results show that fuel consumption can be sufficiently controlled for fulfilling constraints imposed by suppliers. It has been observed from the comparison that the suggested SGEO has the capability to confer with superior-quality solution.

Heat and power generation augmentation planning of isolated microgrid

Abstract- Due to raising heat and power demand the microgrid has to be expanded to meet increased power and heat demand. A short-range heat and power generation augmentation planning (HPGAP) of an isolated microgrid incorporating plug-in electric vehicles (PEVs) is described and evaluated here. The objective of HPGAP is to find out the most cost-effective and reliable expansion plan for meeting the forecasted power demand and heat demand over a short-range horizon while fulfilling a large number of technical, reliability and social constraints. An archetypal test system with existing diesel generators, small hydro power plant (SHPP), solar PV plant, wind turbine generator (WTG), biomass-fuel-fired combined heat and power (BCHP) unit, battery energy storage system (BESS), plug-in electric vehicles (PEVs) and candidate solar PV plant, WTG, SHPP, BCHP units and PEVs is considered here. The problem is solved by using self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE).

An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units

This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24 h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at https://github.com/ebrahimakbary/MNHPSO-JTVAC.

Optimization of a True Moving Bed unit and determination of its feasible operating region using a novel Sliding Particle Swarm Optimization

The optimization of Simulated Moving Bed units has become an important topic to be developed in this field. The present work proposes a novel methodology to optimize a True Moving Bed unit and draw the feasible operating region through a novel improved Self-Organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients and Mutable Searching Region, here called Sliding Particle Swarm Optimization, concomitantly with an adapted method to define the feasible operating region. Two main contributions can be highlighted, first the process optimization with its feasible operational regions, leading to better results when compared with the numerical optimization based on the equilibrium theory, second a new particle swarm method is presented which can deal more efficiently with multi local minima problems. Finally, the concept of feasible operating region concept is presented as a support tool for the process operation. Three different operational scenarios were simulated here in order to verify the consistence and efficiency of the methodology. The main advantage of the methodology here proposed is the possibility to tracking all the possible operating regime of the unit while meeting a given process requirement.

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.

Security Constrained Optimal Power Flow Using Self-Organizing Hierarchical PSO with Time-Varying Acceleration Coefficients

This paper proposes a self-organizing hierarchical particle swarm optimization (SOHPSO) with time-varying acceleration coefficients (TVAC) for solving security constrained optimal power flow (SCOPF) problem. The SCOPF is to minimize a total generation cost while satisfying generator operational constraints and network security constraints in which the minimum severity index is used to alleviate line loading. The proposed SOHPSO-TVAC re-initializes particle velocities whenever the population stagnates at local optima during the search and TVAC is used to enhance convergence to the global optimum solution. Test results on the IEEE 30 and 118 bus systems indicate that the proposed SCOPF renders a lower total generation cost in a faster convergence rate than the other heuristic methods, which is favorable for online implementation.

Congestion Management under Hybrid Electricity Market using Self-organizing Hierarchical Particle Swarm Optimization

This paper proposes an optimal congestion management approach under hybrid electricity market using Self organizing hierarchical particle swarm optimization with Time Varying Acceleration Coefficients (SPSO-TVAC). The aim of the proposed work is to minimize deviations from preferred transaction schedules and hence the congestion cost under hybrid electricity market. The values of Transmission Congestion Distribution factors (TCDFs) are used to select redispatch of generators. Generator reactive power support is considered to lower the congestion cost. Numerical results on IEEE 57 bus system is presented for illustration purpose and the results are compared with Particle swarm optimization (PSO) in terms of solution quality. The comprehensive experimental results prove that the SPSO-TVAC is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem.

Efficient optimum solution for high strength Al alloys matrix composites

Abstract This papers describes a search technique to optimize the fabrication process conditions of the high wear resistance Al alloy matrix composites reinforced with different volume percentages of boron carbide particles. An experimental investigation was then carried out on the abrasive wear behavior of the composites in terms of abrasive particle size, weight fraction and applied load in pin-on-disc type of wear machine. Particle swarm optimization suffers from premature convergence problems when applied to high-dimensional problems, which results in a low optimization precision or even failure. In this study, a self-organizing hierarchical particle swarm optimizer based on the shrinking strategy is introduced for high dimensional function processing optimization of Al matrix composites in order to avoid the premature convergence effectively.

A New Improved Particle Swarm Optimization for Solving Nonconvex Economic Dispatch Problems

This paper proposes a new improved particle swarm optimization (NIPSO) for solving nonconvex economic dispatch (ED) problem in power systems including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed NIPSO method is based on the self-organizing hierarchical (SOH) particle swarm optimizer with time-varying acceleration coefficients (TVAC). The self-organizing hierarchical can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. During the optimization process, the performance of TVAC is applied for properly controlling both local and global explorations with cognitive component and social component of the swarm to obtain the optimum solution accurately and efficiently. The proposed NIPSO algorithm is tested in different types of non-smooth cost functions for solving ED problems and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed NIPSO method is effective and feasible in finding higher quality solutions for non-smooth ED problems than many other methods.

Searching for a novel optimization strategy in tensile and fatigue properties of alumina particulates reinforced aluminum matrix composite

Development and application of metal matrix composite materials and increased application of calculations, simulations and modeling in the area of semi-solid solidification ask for the knowledge of compocasting for these materials. In this study, a self-organizing hierarchical particle swarm optimizer is implemented for computational modeling and optimization of the compocast high strength and highly uniform Al matrix composites. The matrix of the composite was a 6061 Al alloy and the reinforcement was alumina particle (Al2O3p). Experimental results were obtained for hardness, tensile and fatigue properties of the Al alloys with different vol.% of micro-particles. The tensile strength of the composites increased considerably by increasing the reduction ratio in the cold rolling process. It is observed that the presence of reinforcement in the Al alloy degrades the low-cycle fatigue property when the Al matrix composites are subject to strain-controlled cyclic loading. The method combines position update rules, the standard velocity and the strengths of particle swarm optimization with the ideas of selection, crossover and mutation from GA.

Solution of economic load dispatch problem via hybrid particle swarm optimization with time-varying acceleration coefficients and bacteria foraging algorithm techniques

In this research, a hybrid particle swarm optimization with time-varying acceleration coefficients (HPSOTVAC) and bacteria foraging algorithm (BFA) are presented for solving a complex economic load dispatch problem. Basically, there are many realistic constraints that affect feasible operation such as generation limitation, ramp rate limits, prohibited operating zone, nonlinear cost functions, and transmission loss that are considered in this research. The effectiveness of the proposed HPSOTVAC/BFA is tested in 6-, 15-, and 40-unit generating systems. Also, for 6-unit case study, the valve-point effect is considered too. The numerical results demonstrate that the proposed hybrid technique gives less total generation costs than genetic algorithm (GA), particle swarm optimization (PSO), hybrid GA/PSO, active power optimization, iteration PSO, and self-organizing hierarchical PSO methods. Copyright © 2012 John Wiley & Sons, Ltd.