Plant Growth Simulation Algorithm Research Articles

Research on Competitive Multi-facility Location with Market Expansion

It is critical to locate facilities in the retail operations, especially in competitive environment. In most competitive location models available in the literatures, demand is assumed to be fixed or random regardless of market conditions. However, demand is related to the number of facilities, service capacity or the distance from the facility to the customer, which is considered in few literatures. In this paper, a competitive multi-facility location model with fixed demand is established and then market expansion is taken into account in the model. The Plant Growth Simulation Algorithm (PGSA) is put forward to solve the two models and then a small numerical example is given. Compared to the problem with fixed demand, the optimum solution of facility location is variable and the profit increases obviously with market expansion considered.

Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms

Abstract In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software.

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

Bilevel multi-objective construction site security planning with twofold random phenomenon

The lack of early security arrangements during the construction period can increase the vulnerability of construction project. To support current construction security standards, this paper proposes a bilevel multi-objective model for construction site security problem (CSSP). In contrast to prior studies of CSSP, the bilevel relationship and twofold random phenomenon are considered. Specifically, the upper level programming denotes that project security officer must first decide which facilities to be secured under limited funds whilst maximizing the efficiency of the construction facilities system and minimizing the countermeasure cost and economic loss. The lower level programming denotes that the attacker will destroy a subset of the facilities to inflict maximum loss of efficiency in the construction facilities system. To deal with the uncertainties, expected value method and chance constraint method are introduced to transform the uncertain model into a calculable one. Thereafter, a stochastic simulation based constraint checking procedure is designed. Plant Growth Simulation Algorithm (PGSA) is applied to solve this model. Finally, the approach is carried out in the Longtan hydropower construction project to illustrate the efficiency of the proposed model and algorithm.

Investment Decision Model of Industrial Technologies and Innovation Strategic Alliance Regarding Conflict based on Plant Growth Simulation Algorithm

Conflict is the key factor to affect investment and benefits of the industry technologies and innovation strategic alliance. This paper constructs an investment decision model of industry technology innovation strategic alliance by discussing the influence of the conflict on the alliance generic technology investment and the impact of member benefits. The results suggest that maximize revenue optimization of alliance generic technology investment and members can be realized through a investment portfolio of alliance members. Moderate conflict would promote alliance innovation performa nce effectively.

Research on group decision making with interval numbers based on plant growth simulation algorithm

Purpose – This paper aims at multi-attribute and multi-program group decision making when the attribute weights are completely unknown and the attribute value information is in the form of the interval number. Design/methodology/approach – This is an artificial intelligence algorithm for designing information gathering in group decision making. The authors propose the nonlinear programming model to gather information based on plant growth simulation algorithm (PGSA). The authors collect each program on each attribute group decision preference ordering interval and then use them to find the preference vector and the preference matrix. The entropy method is used to determine the weight of each attribute by the constructed preference matrix. According to the possibility degree matrix of each attribute, the combined effect vector is established by the priority weight vector method, which sorts and selects the best decision making program. Findings – To the authors' knowledge, the application of PGSA in the field of management decisions to collect program on each attribute group decision making preference interval number is the first trial in literature. It has retained more valuable decision making information from all experts without distortion. Practical implications – In practice, a real number may not be an accurate representation, but only gives a range of values to describe the attributes. This study provides a useful measurement of interval number information for managers to evaluate military science, venture capital, and environmental assessment, etc. Originality/value – The methodology considers the complete information to ensure no information distortion even with large and complex systems. The authors adopt computer artificial intelligence algorithms to obtain the objective evaluation, which is meaningful for both research studies and practical use.

Optimal allocation of capacitors in radial/mesh distribution systems using mixed integer nonlinear programming approach

Capacitors in radial/mesh distribution systems are used to supply reactive power to minimize loss and to improve the voltage profile. The appropriate placement of capacitors is also important to ensure that system power losses and total investment capacitor costs are minimal. The capacitor placement problem consists of finding specific sitting and sizing to install capacitor banks in an electrical distribution system. Consequently, the losses are reduced due to the compensation of the reactive component of power flow. This paper presents a new mixed integer nonlinear programming approach for capacitor placement in radial/mesh distribution systems that determine the optimal sitting and sizing of capacitors with an objective of reduction power loss and investment capacitor costs. The proposed method is applied to 10, 34, and 85-bus radial distribution systems and CIVANLAR mesh distribution system. Various-scale application systems are used to compare the performance of the proposed method with the Fuzzy reasoning, particle swim optimization (PSO), plant growth simulation algorithm (PGSA), and Heuristic based. Numerical results show that the performance of the proposed MINLP method is better than the other methods. Also, the MINLP method is superior to some other methods in terms of solution power loss and costs.

A Novel Wireless Sensor Network Localization Approach: Localization based on Plant Growth Simulation Algorithm

This paper proposes a novel wireless sensor network (WSN) localization algorithm. Accurate localization is the key technology of WSN. From various aspects many scholars consider localization problem, which is more effective to regard WSN localization problem as an optimization problem. Plant growth simulation algorithm (PGSA) is a kind of new intelligent optimization algorithm, which is intelligent simulation of plant growth in nature way. In addition to the common characteristics of intelligent algorithms, PGSA could be good robustness and guarantee the global optimal solution, etc. In this paper, further enhances the algorithm by adding the plant root of adaptive backlight function to effectively improve the computing speed and localization precision. Comparing this algorithm with simulated annealing algorithm (SAA), simulation results show that this algorithm has a higher and more consistent localization precision and faster computing speed. DOI: http://dx.doi.org/10.5755/j01.eee.19.8.5326

A fuzzy k-coverage approach for RFID network planning using plant growth simulation algorithm

Optimization of network planning is important for the performance improvement of the radio frequency identification (RFID) networks before deployment. This paper proposes an efficient approach for RFID network planning based on k-coverage. A k-coverage model, which is formulated as a multi-dimensional optimization problem with constraint conditions, is developed to evaluate the network performance. The plant growth simulation algorithm (PGSA) is used to optimize the RFID networks by determining the optimal adjustable parameters in the model. In the proposed approach, the objective function and the constraints are separately processed and no external control parameter is required. The simulation results demonstrate the effectiveness of the proposed approach using PGSA in comparison to other algorithms.

Application of Enhanced Honey-Bee Mating Optimization Algorithm to Fault Section Estimation in Power Systems

An application of plant growth simulation algorithm-enhanced honey-bee mating optimization is proposed to solve the fault section estimation problem in a power system. This approach is proposed because the local search capability of the honey-bee mating optimization is highly dependent on the parameter decisions. An inappropriate selection of parameters may affect the computation efficiency. This paper hence has added the plant growth simulation algorithm to enhance the honey-bee mating optimization so that the work of external parameter settings can be economized while the overall computation can be improved. In order to validate the effectiveness of this method, we have conducted the approach on practical power systems. Test results confirm the feasibility of this proposed method for the application considered.

Planning for distributed wind generation under active management mode

In the smart grid (SG), the active management (AM) mode will be applied for the connection and operation of distributed generation (DG), which means real time control and management of DG units and distribution network devices based on real time measurements of primary system parameters. In this paper, a novel bi-level programming model for distributed wind generation (DWG) planning under AM mode is put forward. The model takes the maximum expectation of net benefit of DWG as the upper level program objective, and takes the minimum expectation of generation curtailment as the lower level program objective. The impact of active management algorithm on improvement of branch power flow and node voltage is taken into account. A hybrid algorithm combining the plant growth simulation algorithm (PGSA) with probabilistic optimal power flow (POPF) algorithm is presented to solve the optimal planning of DWG under AM mode. The case studies have been carried out on a 33-node distribution network, and the results verify the rationality of the planning model and the effectiveness of the proposed method.

Network Reconfiguration of Unbalanced Distribution System through Hybrid Heuristic Technique

Electrical power distribution systems are critical links between the utility and customer. They are constructed by one of the three types: radial, open loop and network. They are usually arranged to be radial in operation to simplify over-current protection. Usually, distribution systems are designed to be most efficient at peak load demand. Utilities are constantly looking for newer technologies that enhance power delivery performance. One of the several important issues is the control of power loss. Several strategies can be employed to minimize power losses in a distribution network. They are inclusion of capacitor banks, phase balancing, feeder reconfiguration of distribution system, etc. Among these, feeder reconfiguration helps to operate the distribution system at minimum cost and at the same time improves the system reliability and security. Network reconfiguration is the process of changing the topology of distribution system by altering the open/closed status of switches to find a radial operating structure that minimizes the system real power loss while satisfying operating constraints. This paper proposes an efficient algorithm based on Plant Growth Simulation Algorithm (PGSA), Greedy and Fuzzy. The optimization approach based on PGSA provides detailed description on switch states for calculation. The inclusion of Greedy improves the efficiency of optimization by reducing the number of load flow execution. In addition, heuristic fuzzy has been incorporated to handle constraints along with objective. With the use of proposed algorithm, the system loss has been reduced convincingly, composes proper loading at the branches and make up buses voltage within the limit and which provides solution under different conditions such as normal and abnormal conditions of the system. Furthermore, the solution algorithm is implemented through J2EE (Java 2 Enterprise Edition) architecture to reduce software couplings and to achieve software reusability. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to unbalanced standard 25- bus distribution system and modified IEEE- 125 bus distribution system.

Distribution System Loss Reduction through Hybrid Heuristic Technique

This paper proposes a hybrid technology to solve the distribution system reconfiguration problem. The technology with the mixture of Plant Growth Simulation Algorithm (PGSA), Greedy and heuristic based fuzzy operation has been proposed. The optimization approach based on PGSA provides detailed description on switch states for calculation. The inclusion of Greedy with PGSA improves the efficiency of optimization by identifying the best loop sequence. Furthermore, the heuristic fuzzy has been introduced with PGSA and Greedy for handling constraints amid optimization. With the use of proposed algorithm, the system loss has been reduced convincingly without compromising the power flow constraints. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to IEEE 33 bus distribution system and 83 bus Distribution system of Taiwan Power Company.

Optimal synthesis of linear antenna arrays using a harmony search algorithm

A method based on harmony search algorithm (HSA) for the pattern synthesis of linear antenna arrays with the prescribed nulls is presented. Nulling of the pattern is achieved by controlling the amplitude-only, the phase-only, and the position-only. The HSA is conceptualized using the musical process of searching for a perfect state of harmony. To show the effectiveness of the proposed HSA, several examples of linear antenna array patterns with the imposed single, multiple and broad nulls are given. It is found that the nulling method based on HSA is capable of steering the array nulls precisely to the undesired interference directions. The results of HSA are compared with the results of the modified touring ant colony optimization (MTACO), the bees algorithm (BA), the bacterial foraging algorithm (BFA), the plant growth simulation algorithm (PGSA), the clonal selection algorithm (CLONALG), the particle swarm optimization (PSO), the quadratic programming method (QPM), the tabu search algorithm (TSA), the genetic algorithm (GA), the memetic algorithm (MA), the nondominated sorting genetic algorithm 2 (NSGA-2), the multiobjective differential evolution (MODE), and the multiobjective evolutionary algorithms based on decomposition with differential evolution (MOEA/D-DE).

Optimal capacitor placement in a radial distribution system using Plant Growth Simulation Algorithm

This paper presents a new and efficient approach for capacitor placement in radial distribution systems that determine the optimal locations and size of capacitor with an objective of improving the voltage profile and reduction of power loss. The solution methodology has two parts: in part one the loss sensitivity factors are used to select the candidate locations for the capacitor placement and in part two a new algorithm that employs Plant Growth Simulation Algorithm (PGSA) is used to estimate the optimal size of capacitors at the optimal buses determined in part one. The main advantage of the proposed method is that it does not require any external control parameters. The other advantage is that it handles the objective function and the constraints separately, avoiding the trouble to determine the barrier factors. The proposed method is applied to 10, 34, and 85-bus radial distribution systems. The solutions obtained by the proposed method are compared with other methods. The proposed method has outperformed the other methods in terms of the quality of solution.

Voltage regulator placement in radial distribution system using plant growth simulation algorithm

This paper presents selection of optimal locations and sizes of voltage regulators in radial distribution system using Plant Growth Simulation Algorithm (PGSA). Voltage has always been considered as an integral part of the power system response. There are several factors, which contribute to voltage collapse such as increased loading on distribution feeders, reactive power constraints, on load tap changer dynamics and load characteristics. The proposed method for voltage regulator placement is suitable for large radial distribution network. The VR problem consists of two sub problems, that of optimal placement and optimal choice of tap setting. The proposed method deals with initial selection of voltage regulator buses by using power loss indices (PLI). The candidate node identification technique and Plant Growth Simulation Algorithm (PGSA) are used for optimal location and number along with tap setting of the voltage regulators that maintain a smooth voltage profile throughout the network. The aim of this paper is to obtain optimal voltage control with voltage regulators and then to decrease the total cost and losses, to obtain the maximum net savings. The effectiveness of the proposed method is illustrated with 33 bus and 69 bus radial distribution systems and these results are compared with Discrete Particle Swarm Optimization method. International Journal of Engineering, Science and Technology, Vol. 2, No. 6, 2010, pp. 207-217

Feeder Reconfiguration and Service Restoration in Distribution Networks Through Fusion Technology: Part 2

Distribution network optimisation problem is realised by changing the status of sectionalising switches, and is usually done for loss reduction and/or load balancing of the system. In this paper, Fusion technology has been introduced to solve the optimisation problem. Fusion technology is the mixture of plant growth simulation algorithm (PGSA), Greedy and heuristic based fuzzy operation. The optimisation approach based on PGSA provides detailed description on switch states for calculation. The inclusion of Greedy with PGSA improves the efficiency of optimisation by identifying the best loop sequence. Furthermore, the heuristic fuzzy has been introduced with PGSA and Greedy for handling constraints amid optimisation. With the use of proposed algorithm, the system loss has been reduced convincingly, composes proper loading at the branches and make up buses voltage within the limit and which provides solution under all the conditions of the system. Furthermore, the solution algorithm is implemented through J2EE (Java 2 Enterprise Edition) architecture to reduce software couplings and to achieve software reusability. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to IEEE 33-bus distribution system and 83-bus distribution system of Taiwan Power Company.

Radial Distribution Network Reconfiguration for Loss Reduction and Load Balancing using Plant Growth Simulation Algorithm

Network reconfiguration is a combinatorial optimization problem because it accounts various operational constraints in distribution systems. Plant Growth Simulation Algorithm has emerged as a useful optimization tool for handling nonlinear programming problems. In this paper, Plant Growth Simulation Algorithm has been proposed with a view to enhance speed and robustness. This method has been applied successfully on some benchmark mathematical problems. The solution for loss reduction through network reconfiguration involves a guiding search over the relevant configurations. The branch load balancing index and system load balancing index are defined for formulation of objective function of load balancing problem. Most of the methods published in the literature require external parameters such as barrier factors, crossover rate etc. The main advantage of the method presented here does not require external parameters. The proposed method is tested to reconfigure 69-node radial distribution system for loss minimization and load balancing.

Optimal Capacitor Allocation for loss reduction in Distribution System Using Fuzzy and Plant Growth Simulation Algorithm

This paper presents a new and efficient approach for capacitor placement in radial distribution systems that determine the optimal locations and size of capacitor with an objective of improving the voltage profile and reduction of power loss. The solution methodology has two parts: in part one the loss sensitivity factors are used to select the candidate locations for the capacitor placement and in part two a new algorithm that employs Plant growth Simulation Algorithm (PGSA) is used to estimate the optimal size of capacitors at the optimal buses determined in part one. The main advantage of the proposed method is that it does not require any external control parameters. The other advantage is that it handles the objective function and the constraints separately, avoiding the trouble to determine the barrier factors. The proposed method is applied to 9 and 34 bus radial distribution systems. The solutions obtained by the proposed method are compared with other methods. The proposed method has outperformed the other methods in terms of the quality of solution. Keywords—Distribution systems, Capacitor allocation, Loss reduction, Fuzzy, PGSA.

Distribution Network Optimisation Through Fusion Technology

In this paper, fusion technology is introduced to solve the distribution network optimisation problem. The mixture of plant growth simulation algorithm (PGSA), neural networks and heuristic fuzzy are combined together to form fusion technology. The optimisation approach based on PGSA provides a detailed description on switch states for calculation. The inclusion of neural networks with PGSA improves the efficiency of optimisation by avoiding repeated load flow calculation. Furthermore, heuristic fuzzy has been introduced with PGSA and neural networks for handling more constraints in optimisation. The effectiveness of the proposed approach is demonstrated by applying the feeder switching operation scheme to a standard sample system.