A Guidable Bat Algorithm Based on Doppler Effect to Improve Solving Efficiency for Optimization Problems

2014 ◽  
pp. 373-383 ◽  
Author(s):  
Yi-Ting Chen ◽  
Chin-Shiuh Shieh ◽  
Mong-Fong Horng ◽  
Bin-Yih Liao ◽  
Jeng-Shyang Pan ◽  
...  
Keyword(s):  
Doppler Effect ◽  
Optimization Problems ◽  
Bat Algorithm

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.

A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems

2013 ◽  
Vol 464 ◽  
pp. 352-357
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computation Time ◽  
Finite Sequence ◽  
Bat Algorithm ◽  
Harmony Search Algorithm ◽  
Response Surfaces ◽  
Sample Mean ◽  
Constrained Problems

The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search process and social behavior of bat echolocation for decision direction. These algorithms were implemented under different natures of three optimization, which are single-peak, multi-peak and curved-ridge response surfaces. Moreover, both algorithms were also applied to constrained engineering problems. The results from non-linear continuous unconstrained functions in the context of response surface methodology and constrained problems can be shown that Bat algorithm seems to be better in terms of the sample mean and variance of design points yields and computation time.

Metaheuristic Approaches to Task Consolidation Problem in the Cloud

2017 ◽  
pp. 168-189 ◽  
Author(s):  
Sambit Kumar Mishra ◽  
Bibhudatta Sahoo ◽  
Kshira Sagar Sahoo ◽  
Sanjay Kumar Jena
Keyword(s):  
Combinatorial Optimization ◽  
Distributed Computing ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Allocation Problem ◽  
Service Allocation ◽  
Multidimensional Knapsack ◽  
Np Hard Problem ◽  
Metaheuristic Techniques

The service (task) allocation problem in the distributed computing is one form of multidimensional knapsack problem which is one of the best examples of the combinatorial optimization problem. Nature-inspired techniques represent powerful mechanisms for addressing a large number of combinatorial optimization problems. Computation of getting an optimal solution for various industrial and scientific problems is usually intractable. The service request allocation problem in distributed computing belongs to a particular group of problems, i.e., NP-hard problem. The major portion of this chapter constitutes a survey of various mechanisms for service allocation problem with the availability of different cloud computing architecture. Here, there is a brief discussion towards the implementation issues of various metaheuristic techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), BAT algorithm, etc. with various environments for the service allocation problem in the cloud.

A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems

2018 ◽  
Vol 51 (2) ◽  
pp. 265-285 ◽  
Author(s):  
Abdulbaset Saad ◽  
Zuomin Dong ◽  
Brad Buckham ◽  
Curran Crawford ◽  
Adel Younis ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Black Box ◽  
Computationally Expensive

scholarly journals GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guoming Du ◽  
Yangbo Chen ◽  
Wei Sun
Keyword(s):  
Optimization Problems ◽  
Bat Algorithm ◽  
Population Diversity ◽  
Premature Convergence ◽  
Local Optimum ◽  
Guangzhou City ◽  
Location Allocation ◽  
Geographic Space ◽  
Spatial Search ◽  
Gis Technique

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

A novel bat algorithm with dynamic membrane structure for optimization problems

2020 ◽  
Author(s):  
Bisan Alsalibi ◽  
Laith Abualigah ◽  
Ahamad Tajudin Khader
Keyword(s):  
Membrane Structure ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Dynamic Membrane

A Survey: Bat Algorithm and Its Application to provide optimal solutions for optimization Problems

2016 ◽  
Vol 38 (3) ◽  
pp. 129-133
Author(s):  
Gajendra Kumar Ahirwar ◽  
◽  
Sachin Goyal ◽  
Nishchol Mishra ◽  
Ratish Agrawal
Keyword(s):  
Optimization Problems ◽  
Bat Algorithm ◽  
Optimal Solutions
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