Modified Global Flower Pollination Algorithm and its Application for Optimization Problems

2018 ◽  
Vol 11 (3) ◽  
pp. 496-507 ◽  
Author(s):  
Moh’d Khaled Yousef Shambour ◽  
Ahmed A. Abusnaina ◽  
Ahmed I. Alsalibi
Keyword(s):  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Flower Pollination

scholarly journals Optimal Power Flow Using Flower Pollination Algorithm: A Case Study of 500 kV Java-Bali Power System

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Fredi Prima Sakti ◽  
Sarjiya Sarjiya ◽  
Sasongko Pramono Hadi
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Optimal Power ◽  
Metaheuristic Methods ◽  
Owned Company

Flower Pollination Algorithm (FPA) is one of metaheuristic methods that is widely used in optimization problems. This method was inspired by the nature of flower pollination. In this research, FPA is applied to solve Optimal Power Flow (OPF) problems with case study of 500 kV Java-Bali power system in Indonesia. The system consists of 25 bus with 30 lines and 8 generating units. Control variables are generation of active power and voltage magnitude at PV bus and swing bus under several power system constraints. The results show that FPA method is capable of solving OPF problem. This method decreased the generator fuel cost of PT. PLN (Persero), the state-owned company in charge of providing electricity in Indonesia, up to 13.15%.

scholarly journals An improved flower pollination solution for economic dispatch with valve point effect

2021 ◽  
Vol 22 (2) ◽  
pp. 629
Author(s):  
Muhammad Iqbal Kamboh ◽  
Nazri Mohd Nawi ◽  
Radiah Bt. Mohamad
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Thermal Power ◽  
Economic Dispatch ◽  
Evolutionary Programming ◽  
Flower Pollination Algorithm ◽  
Cost Curve ◽  
Fuel Cost ◽  
Flower Pollination ◽  
Valve Point Effect

<span>The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions.  However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems.  The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.</span>

scholarly journals Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Weijia Cui ◽  
Yuzhu He
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Orthogonal Experiment ◽  
Optimization Technique ◽  
Iteration Process ◽  
Population Diversity ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Effect Mechanism ◽  
Orthogonal Learning

The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

scholarly journals Flower Pollination Algorithm Combined with Multiple Strategies of Opposition–Based Learning

2018 ◽  
Author(s):  
Cácio L. N. A. Bezerra ◽  
Cácio L. N. A. Bezerra ◽  
Fábio G. B. C. Costa ◽  
Lucas V. Bazante ◽  
Pedro V. M. Carvalho ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Local Optimum ◽  
Multiple Strategies ◽  
Flower Pollination ◽  
Opposition Based Learning ◽  
New Variant

Flower Pollination Algorithm (FPA) has been widely used to solve optimization problems. However, it faces the problem of stagnation in local optimum. Several approaches have been proposed to deal with this problem. To improve the performance of the FPA, this paper presents a new variant that combines FPA and two variants of the Opposition Based Learning (OBL), such as Quasi OBL (QOBL) and Elite OBL (EOBL). To evaluate this proposal, 10 benchmark functions were used. In addition, the proposed algorithm was compared with original FPA and three variants such as FA–EOBL, SBFPA and DE–FPA. The proposal presented significant results.

scholarly journals Flower Pollination Algorithm for Adaptive Beamforming of Phased Array Antennas

2017 ◽  
Vol 2 (2) ◽  
pp. 1-5
Author(s):  
Tarek Abdel Rahman Sallam ◽  
Adel Bedair Abdel-Rahman ◽  
Masoud Alghoniemy ◽  
Zen Kawasaki
Keyword(s):  
Phased Array ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Adaptive Beamforming ◽  
Array Antenna ◽  
Flower Pollination Algorithm ◽  
Phased Array Antenna ◽  
Phased Array Antennas ◽  
Flower Pollination ◽  
Array Antennas

This paper introduces the flower pollination algorithm (FPA) as an optimization technique suitable for adaptive beamforming of phased array antennas. The FPA is a new nature-inspired evolutionary computation algorithm that is based on pollinating behaviour of flowering plants. Unlike the other nature-inspired algorithms, the FPA has fewer tuning parameters to fit into different optimization problems. The FPA is used to compute the complex beamforming weights of the phased array antenna. In order to exhibit the robustness of the new technique, the FPA has been applied to a uniform linear array antenna with different array sizes. The results reveal that the FPA leads to the optimum Wiener weights in each array size with less number of iterations compared with two other evolutionary optimization algorithms namely, particle swarm optimization and cuckoo search.

Solving Multiple Vehicle Routing Problems with Time Constraintsby Flower Pollination Algorithm Optimization

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Ease Of Use ◽  
Computational Time ◽  
Flower Pollination Algorithm ◽  
Combinatorial Optimization Problems ◽  
Flower Pollination ◽  
Total Distance

The multiple vehicle routing problem (MVRP) with the time constraint is one of the most importantreal-world problems in industrial and logistic engineering. The MVRP problems can be considered as a class ofthe non-polynomial (NP) time-complete combinatorial optimization problem. Such the MVRP problems aim tofind the set of routes with the shortest total distance for overall minimum route cost serving all the givendemands by the fleet of vehicles. Based on modern optimization, the MVRP problems can be optimally solvedby the potential metaheuristic optimization techniques. The flower pollination algorithm (FPA) is one of themost efficient metaheuristic optimizers proposed for solving the combinatorial optimization problems. Withfew searching parameters, the algorithm of the FPA is not complex and ease of use. In this paper, the FPA isapplied to solve five selected benchmark MVRP problems with the time constraints consisting of 50-100destinations. Results obtained by the FPA will be compared with those obtained by genetic algorithm (GA), tabusearch (TS) and particle swarm optimization (PSO). From results, the FPA can provide optimal solutions of allfive selected problems. Optimal results obtained by the FPA are superior to PSO, TS and GA, respectively,with shorter total distance and computational time consumed

A Hybrid Algorithm Based on Flower Pollination Algorithm and Electro Search for Global Optimization

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Md Fadil Md Esa ◽  
Noorfa Haszlinna Mustaffa ◽  
Nor Haizan Mohamed Radzi
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
High Performance ◽  
Search Strategy ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Method ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Local Search Strategy

In this paper, we have presented a new hybrid optimization method called hybrid Electro-Search algorithm (Eo) and Flower Pollination Optimization Algorithm (FPA) which introduces Eo to FPA. EO-FPA combines the merits of both Eo and FPA by designing on the local-search strategy from Eo and global-search strategy from FPA. The results of the experiments performed with twenty-two well-known benchmark functions show that the proposed algorithm possesses outstanding performance in statistical merit as compared to the original and variant FPA. It is proven that the EO-FPA algorithm requires better formulation to achieve efficiency and high performance to work out with global optimization problems.

Sign in / Sign up

Export Citation Format

Share Document