scholarly journals A Novel Crow Search Algorithm Based on Improved Flower Pollination

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Qian Cheng ◽  
Huajuan Huang ◽  
Minbo Chen
Keyword(s):  
Search Algorithm ◽  
Function Optimization ◽  
Flower Pollination Algorithm ◽  
Benchmark Problems ◽  
Local Optimum ◽  
Location Update ◽  
Flower Pollination ◽  
Cauchy Mutation ◽  
Speed Reducer ◽  
Swarm Intelligence Optimization

Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows’ intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem.

scholarly journals Pattern Synthesis of Linear Antenna Arrays Using Enhanced Flower Pollination Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Urvinder Singh ◽  
Rohit Salgotra
Keyword(s):  
Antenna Arrays ◽  
Flower Pollination Algorithm ◽  
Benchmark Problems ◽  
Linear Antenna ◽  
Exploration And Exploitation ◽  
Pattern Synthesis ◽  
Flower Pollination ◽  
New Variant ◽  
Cauchy Mutation ◽  
Linear Antenna Arrays

In this paper, a new variant of flower pollination algorithm (FPA), namely, enhanced flower pollination algorithm (EFPA), has been proposed for the pattern synthesis of nonuniform linear antenna arrays (LAA). The proposed algorithm uses the concept of Cauchy mutation in global pollination and enhanced local search to improve the exploration and exploitation tendencies of FPA. It also uses dynamic switching to control the rate of exploration and exploitation. The algorithm is tested on standard benchmark problems and has been compared statistically with state of the art to prove its worthiness. LAA design is a tricky and difficult electromagnetic problem. Hence to check the efficacy of the proposed algorithm it has been used for synthesis of four different LAA with different sizes. Experimental results show that EFPA algorithm provides enhanced performance in terms of side lobe suppression and null control compared to FPA and other popular algorithms.

scholarly journals Adaptive Spiral Flying Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chengtian Ouyang ◽  
Yaxian Qiu ◽  
Donglin Zhu
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Standard Test ◽  
Local Optimum ◽  
Robot Path Planning ◽  
Stable Path ◽  
New Type ◽  
The Individual ◽  
Swarm Intelligence Optimization ◽  
Robot Path

The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.

scholarly journals A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Yuting Lu ◽  
Yongquan Zhou ◽  
Xiuli Wu
Keyword(s):  
Engineering Design ◽  
Simplex Method ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Function Optimization ◽  
Local Optimum ◽  
Computational Accuracy ◽  
Design Problems ◽  
Opposition Based Learning ◽  
Engineering Design Problems

In this paper, a novel hybrid lightning search algorithm-simplex method (LSA-SM) is proposed to solve the shortcomings of lightning search algorithm (LSA) premature convergence and low computational accuracy and it is applied to function optimization and constrained engineering design optimization problems. The improvement adds two major optimization strategies. Simplex method (SM) iteratively optimizes the current worst step leaders to avoid the population searching at the edge, thus improving the convergence accuracy and rate of the algorithm. Elite opposition-based learning (EOBL) increases the diversity of population to avoid the algorithm falling into local optimum. LSA-SM is tested by 18 benchmark functions and five constrained engineering design problems. The results show that LSA-SM has higher computational accuracy, faster convergence rate, and stronger stability than other algorithms and can effectively solve the problem of constrained nonlinear optimization in reality.

scholarly journals Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls

2021 ◽  
Vol 13 (4) ◽  
pp. 1639
Author(s):  
Melda Yücel ◽  
Aylin Ece Kayabekir ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli ◽  
Sanghun Kim ◽  
...  
Keyword(s):  
Reinforced Concrete ◽  
Structural Engineering ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Retaining Walls ◽  
Comparative Investigation ◽  
Flower Pollination Algorithm ◽  
Local Optimum ◽  
Population Number

In the optimum design of reinforced concrete (RC) structural members, the robustness of the employed method is important as well as solving the optimization problem. In some cases where the algorithm parameters are defined as non-effective values, local-optimum solutions may prevail over the existing global optimum results. Any metaheuristic algorithm can be effective to solve the optimization problem but must give the same results for several runs. Due to the randomization nature of these algorithms, the performance may vary with respect to time. The essential and novel work done in this study is the comparative investigation of 10 different metaheuristic algorithms and two modifications of harmony search (HS) algorithm on the optimum cost design of RC retaining walls constrained with geotechnical and structural state limits. The employed algorithms include classical ones (genetic algorithm (GA), differential evaluation (DE), and particle swarm optimization (PSO)), proved ones on structural engineering applications (harmony search, artificial bee colony, firefly algorithm), and recent algorithms (teaching–learning-based optimization (TLBO), flower pollination algorithm (FPA), grey wolf optimization, Jaya algorithm (JA)). The modifications of HS include adaptive HS (AHS) concerning the automatic change of algorithm parameters and hybridization of AHS with JA that is developed for the investigated problem. According to the numerical investigations, recent algorithms such as TLBO, FPA, and JA are generally the best at finding the optimum values with less deviation than the others. The adaptive-hybrid HS proposed in this study is also competitive with these algorithms, while it can reach the best solution by using a lower population number which can lead to timesaving in the optimization process. By the minimization of material used in construction via best optimization, sustainable structures that support multiple types of constraints are provided.

General Review of Calibration Process of Nonlinear Muskingum Model and Its Optimization by Up-to-Date Methods

2020 ◽  
pp. 61-83
Author(s):  
Umut Kırdemir ◽  
Umut Okkan
Keyword(s):  
Water Cycle ◽  
Search Algorithm ◽  
Flood Routing ◽  
Flower Pollination Algorithm ◽  
Model Parameters ◽  
Flower Pollination ◽  
Muskingum Model ◽  
Colliding Bodies Optimization ◽  
Nonlinear Muskingum Model ◽  
Outflow Hydrograph

Nonlinear Muskingum method is a very efficient tool in flood routing implementation. It is possible to estimate an outflow hydrograph by a given inflow hydrograph of a flood at a specific point of the river channel. However, it turns out an optimization problem at the stage of employing this method, and it becomes important to reach the optimal model parameters so as to obtain precise outflow hydrograph estimations. Hence, it was decided to utilize five up-to-date optimization algorithms, namely, vortex search algorithm (VSA), gases brownian motion algorithm (GBMO), water cycle algorithm (WCA), flower pollination algorithm (FPA), and colliding bodies optimization (CBO). The algorithms were integrated with the nonlinear Muskingum model so as to estimate the outflow hydrograph of Wilson data, and it was deduced that WCA, FPA, and VSA perform relatively better than the models employed in the other researches before.

scholarly journals Flower Pollination Algorithm for the Inversion of Schlumberger Sounding Curve

2021 ◽  
Vol 873 (1) ◽  
pp. 012018
Author(s):  
F Raflesia ◽  
W Widodo
Keyword(s):  
Synthetic Data ◽  
Least Square ◽  
Flower Pollination Algorithm ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Initial Model ◽  
Linear Inversion ◽  
Flower Pollination ◽  
Non Linear ◽  
Schlumberger Sounding

Abstract Inversion of schlumberger sounding curve is non-linear, and multi-minimum. All linear inversion strategies can produce local optimum, and depend on the initial model. Meanwhile, the non-linear bionic method for inversion problems does not require an initial model, simple, flexible, derivation-free mechanism and can avoid local optimum. One of the new algorithm of the non-linear bionic method for geophysical inversion problem is the Flower Pollination Algorithm (FPA). The FPA is used for the inversion of schlumberger sounding curve. This algorithm was stimulated by the pollination process for blooming plants. The applicability of the present algorithm was tested on synthetic models A-type and KH-type curve. Numerical tests in MATLAB R2013a for the synthetic data and the observed data show that FPA can find the global minimum. For further study, inverted results using the FPA are contrasted with the damped least-square (DLSQR) inversion program, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The outcomes of the comparison reveal that FPA performs better than the DLSQR inversion program, PSO, and GWO.

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.

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