An enhanced Bat algorithm with mutation operator for numerical optimization problems

2017 ◽  
Vol 31 (S1) ◽  
pp. 617-651 ◽  
Author(s):  
Waheed A. H. M. Ghanem ◽  
Aman Jantan
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Mutation Operator

scholarly journals A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
Optimization Approach ◽  
Global Numerical Optimization ◽  
Hybrid Metaheuristic ◽  
Real World Applications

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

scholarly journals A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 135 ◽  
Author(s):  
Yechuang Wang ◽  
Penghong Wang ◽  
Jiangjiang Zhang ◽  
Zhihua Cui ◽  
Xingjuan Cai ◽  
...  
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Global Search ◽  
Excellent Performance ◽  
Multiple Strategies ◽  
Search Capability

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

2011 ◽  
Vol 148-149 ◽  
pp. 134-137 ◽  
Author(s):  
Pei Wei Tsai ◽  
Jeng Shyang Pan ◽  
Bin Yih Liao ◽  
Ming Jer Tsai ◽  
Vaci Istanda
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Computational Cost ◽  
Bat Algorithm ◽  
Experimental Results ◽  
Computational Time ◽  
Test Functions ◽  
Novel Algorithm

Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

A novel improved accelerated particle swarm optimization algorithm for global numerical optimization

2014 ◽  
Vol 31 (7) ◽  
pp. 1198-1220 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir Hossein Gandomi ◽  
Xin-She Yang ◽  
Amir Hossein Alavi
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Content Type ◽  
Accelerated Particle Swarm Optimization

Purpose – Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. Design/methodology/approach – The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence. Findings – A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO. Originality/value – A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

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