An innovative deterministic algorithm for optimal placement of micro phasor measurement units in radial electricity distribution systems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aref Gholizadeh Manghutay ◽  
Mehdi Salay Naderi ◽  
Seyed Hamid Fathi
Keyword(s):  
Distribution Systems ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Optimal Placement ◽  
Electricity Distribution ◽  
Content Type ◽  
Running Time ◽  
Memory Size ◽  
Measurement Units

Purpose Heuristic algorithms have been widely used in different types of optimization problems. Their unique features in terms of running time and flexibility have made them superior to deterministic algorithms. To accurately compare different heuristic algorithms in solving optimization problems, the final optimal solution needs to be known. Existing deterministic methods such as Exhaustive Search and Integer Linear Programming can provide the final global optimal solution for small-scale optimization problems. However, as the system grows the number of calculations and required memory size incredibly increases, so applying existing deterministic methods is no longer possible for medium and large-scale systems. The purpose of this paper is to introduce a novel deterministic method with short running time and small memory size requirement for optimal placement of Micro Phasor Measurement Units (µPMUs) in radial electricity distribution systems to make the system completely observable. Design/methodology/approach First, the principle of the method is explained and the observability of the system is analyzed. Then, the algorithm’s running time and memory usage when applying on some of the modified versions of the Institute of Electrical and Electronics Engineers 123-node test feeder are obtained and compared with those of its deterministic counterparts. Findings Because of the innovative method of step-by-step placement of µPMUs, a unique method is developed. Simulation results elucidate that the proposed method has unique features of short running time and small memory size requirements. Originality/value While the mathematical background of the observability study of electricity distribution systems is very well-presented in the referenced papers, the proposed step-by-step placement method of µPMUs, which shrinks unobservable parts of the system in each step, is not discussed yet. The presented paper is directly applicable to typical problems in the field of power systems.

Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems

2017 ◽  
Vol 6 (3) ◽  
pp. 1-28 ◽  
Author(s):  
Tri Phuoc Nguyen ◽  
Vo Ngoc Dieu ◽  
Pandian Vasant
Keyword(s):  
Distribution Systems ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Active Power ◽  
Optimal Placement ◽  
Sensitivity Factor ◽  
Optimal Size ◽  
Symbiotic Organisms Search ◽  
Symbiotic Relationships

This paper presents a new approach for solving optimal placement of distributed generation (OPDG) problem in distribution systems for minimizing active power loss. In this research, the loss sensitivity factor is used to identify the optimal locations for installation of DGs and symbiotic organisms search (SOS) is used to find the optimal size of DGs. The proposed SOS approach is defined as symbiotic relationships observed between two organisms in the ecosystem, which does not need control parameters like other meta-heuristic algorithms. The OPDG problem is considered with two different scenarios including Scenario I for DGs installed at candidate buses to supply only active power to the system and Scenario II for same as Scenario I except that DGs are controlled to supply both active and reactive powers at a 0.85 p.f. The effectiveness of the proposed SOS method has been verified on the IEEE 33-bus and 69-bus radial distribution systems. The result comparison from the test systems has indicated that the proposed SOS is effective to obtain the optimal solution for the OPDG problem.

Heuristic algorithm for optimal redundancy allocation in complex systems

2019 ◽  
Vol 25 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Sudhanshu Aggarwal
Keyword(s):  
Complex Systems ◽  
Heuristic Algorithm ◽  
Communication Systems ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Optimal Solution ◽  
Systems Design ◽  
Computational Time ◽  
Coherent System ◽  
Content Type

PurposeThe purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.Design/methodology/approachThe algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.FindingsThe proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.Research limitations/implicationsIn general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.Practical implicationsIt is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.Social implicationsReliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.Originality/valueThe utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

Computational Complexity Analysis of Selective Breeding Algorithm

2014 ◽  
Vol 591 ◽  
pp. 172-175
Author(s):  
M. Chandrasekaran ◽  
P. Sriramya ◽  
B. Parvathavarthini ◽  
M. Saravanamanikandan
Keyword(s):  
Computational Complexity ◽  
Selective Breeding ◽  
Heuristic Algorithms ◽  
Complexity Analysis ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Problem Size ◽  
Combinatorial Optimization Problems ◽  
Complete Problems

In modern years, there has been growing importance in the design, analysis and to resolve extremely complex problems. Because of the complexity of problem variants and the difficult nature of the problems they deal with, it is arguably impracticable in the majority time to build appropriate guarantees about the number of fitness evaluations needed for an algorithm to and an optimal solution. In such situations, heuristic algorithms can solve approximate solutions; however suitable time and space complication take part an important role. In present, all recognized algorithms for NP-complete problems are requiring time that's exponential within the problem size. The acknowledged NP-hardness results imply that for several combinatorial optimization problems there are no efficient algorithms that realize a best resolution, or maybe a close to best resolution, on each instance. The study Computational Complexity Analysis of Selective Breeding algorithm involves both an algorithmic issue and a theoretical challenge and the excellence of a heuristic.

An Improved Ant Colony Optimization for VRP with Time Windows

2012 ◽  
Vol 263-266 ◽  
pp. 1609-1613 ◽  
Author(s):  
Su Ping Yu ◽  
Ya Ping Li
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Distribution Systems ◽  
Optimization Problems ◽  
Time Windows ◽  
Optimal Solution ◽  
Ant Colony ◽  
Routing Problem ◽  
Combinatorial Optimization Problems

The Vehicle Routing Problem (VRP) is an important problem occurring in many distribution systems, which is also defined as a family of different versions such as the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The Ant Colony Optimization (ACO) is a metaheuristic for combinatorial optimization problems. Given the ACO inadequacy, the vehicle routing optimization model is improved and the transfer of the algorithm in corresponding rules and the trajectory updated regulations is reset in this paper, which is called the Improved Ant Colony Optimization (I-ACO). Compared to the calculated results with genetic algorithm (GA) and particle swarm optimization (PSO), the correctness of the model and algorithm is verified. Experimental results show that the I-ACO can quickly and effectively obtain the optimal solution of VRFTW.

scholarly journals A hybrid cuckoo–harmony search algorithm for optimal design of water distribution systems

2015 ◽  
Vol 18 (3) ◽  
pp. 544-563 ◽  
Author(s):  
Razi Sheikholeslami ◽  
Aaron C. Zecchin ◽  
Feifei Zheng ◽  
Siamak Talatahari
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Method

Meta-heuristic algorithms have been broadly used to deal with a range of water resources optimization problems over the past decades. One issue that exists in the use of these algorithms is the requirement of large computational resources, especially when handling real-world problems. To overcome this challenge, this paper develops a hybrid optimization method, the so-called CSHS, in which a cuckoo search (CS) algorithm is combined with a harmony search (HS) scheme. Within this hybrid framework, the CS is employed to find the promising regions of the search space within the initial explorative stages of the search, followed by a thorough exploitation phase using the combined CS and HS algorithms. The utility of the proposed CSHS is demonstrated using four water distribution system design problems with increased scales and complexity. The obtained results reveal that the CSHS method outperforms the standard CS, as well as the majority of other meta-heuristics that have previously been applied to the case studies investigated, in terms of efficiently seeking optimal solutions. Furthermore, the CSHS has two control parameters that need to be fine-tuned compared to many other algorithms, which is appealing for its practical application as an extensive parameter-calibration process is typically computationally very demanding.

scholarly journals A bi-stage surrogate-assisted hybrid algorithm for expensive optimization problems

2021 ◽  
Author(s):  
Zhihai Ren ◽  
Chaoli Sun ◽  
Ying Tan ◽  
Guochen Zhang ◽  
Shufen Qin
Keyword(s):  
Local Search ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Second Stage ◽  
Expensive Problems ◽  
Expensive Optimization Problems ◽  
Expensive Optimization

AbstractSurrogate-assisted meta-heuristic algorithms have shown good performance to solve the computationally expensive problems within a limited computational resource. Compared to the method that only one surrogate model is utilized, the surrogate ensembles have shown more efficiency to get a good optimal solution. In this paper, we propose a bi-stage surrogate-assisted hybrid algorithm to solve the expensive optimization problems. The framework of the proposed method is composed of two stages. In the first stage, a number of global searches will be conducted in sequence to explore different sub-spaces of the decision space, and the solution with the maximum uncertainty in the final generation of each global search will be evaluated using the exact expensive problems to improve the accuracy of the approximation on corresponding sub-space. In the second stage, the local search is added to exploit the sub-space, where the best position found so far locates, to find a better solution for real expensive evaluation. Furthermore, the local and global searches in the second stage take turns to be conducted to balance the trade-off of the exploration and exploitation. Two different meta-heuristic algorithms are, respectively, utilized for the global and local search. To evaluate the performance of our proposed method, we conduct the experiments on seven benchmark problems, the Lennard–Jones potential problem and a constrained test problem, respectively, and compare with five state-of-the-art methods proposed for solving expensive problems. The experimental results show that our proposed method can obtain better results, especially on high-dimensional problems.

scholarly journals Critical analysis: bat algorithm-based investigation and application on several domains

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shahla U. Umar ◽  
Tarik A. Rashid
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Survey Study ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Enhanced Particle Swarm Optimization ◽  
Modified Bat Algorithm

Purpose The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms. Design/methodology/approach Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm). Findings Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

A novel PGSA–PSO hybrid algorithm for structural optimization

2019 ◽  
Vol 37 (1) ◽  
pp. 144-160 ◽  
Author(s):  
Zhengrong Jiang ◽  
Quanpan Lin ◽  
Kairong Shi ◽  
Wenzhi Pan
Keyword(s):  
Structural Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Initial Growth ◽  
Global Optimal Solution ◽  
Content Type ◽  
Dome Structure ◽  
Global Optimal ◽  
Optimization Efficiency

Purpose The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and particle swarm optimization hybrid algorithm (PGSA–PSO hybrid algorithm), for solving structural optimization problems. Design/methodology/approach To further enhance the optimization efficiency and precision of this algorithm, the optimization solution process of PGSA–PSO comprises two steps. First, an excellent initial growth point is selected by PSO. Then, the global optimal solution can be obtained quickly by PGSA and its improved strategy called growth space adjustment strategy. A typical mathematical example is provided to verify the capacity of the new hybrid algorithm to effectively improve the global search capability and search efficiency of PGSA. Moreover, PGSA–PSO is applied to the optimization design of a suspended dome structure. Findings Through typical mathematical example, the improved strategy can improve the optimization efficiency of PGSA considerably, and an initial growth point that falls near the global optimal solution can be obtained. Through the optimization of the pre-stress of a suspended dome structure, compared with other methods, the hybrid algorithm is effective and feasible in structural optimization. Originality/value Through the examples of suspended dome structure, it shows that the optimization efficiency and precision of PGSA–PSO are better than those of other algorithms and methods. PGSA–PSO is effective and feasible in structural optimization problems such as pre-stress optimization, size optimization, shape optimization and even topology optimization.

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