Comparative Economic between Bat Algorithm (Ba) and Particle Swarm Optimization (Pso) for Solving Economy Dispatch

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter [Formula: see text] of the pulse frequency [Formula: see text]. Second, we use a dynamic formulation to update the parameter α of the loudness [Formula: see text]. The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and Binary Bat Algorithm (BBA).

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Gene regulatory networks using bat algorithm inspired particle swarm optimization

2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) ◽

10.1109/wiecon-ece.2015.7443946 ◽

2015 ◽

Cited By ~ 1

Author(s):

Abhinandan Khan ◽

Piyali Datta ◽

Rajat Kumar Pal ◽

Goutam Saha

Keyword(s):

Particle Swarm Optimization ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Particle Swarm ◽

Bat Algorithm ◽

Swarm Optimization ◽

Gene Regulatory

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Um Estudo Sobre Meta-Heurísticas em Otimização Multimodal

10.14210/cotb.v11n1.p045-047 ◽

2020 ◽

Author(s):

Omar Andres Carmona Cortes ◽

Lúcio Flávio de Jesus Silva

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Differential Evolution ◽

Particle Swarm ◽

Bat Algorithm ◽

Swarm Optimization ◽

Preliminary Results ◽

Set Up

RESUMOThis paper presents an investigation of four metaheuristics for multimodaloptimization. The algorithms have been implemented inR and compared against each other using well-known benchmarkfunctions. We implemented the following algorithms: Genetic Algorithm(GA), Particle Swarm Optimization (PSO), Differential Evolution(DE), and Bat Algorithm (BA). For comparisons, we used fivemultimodal benchmarks: Rosenbrock, Griewank, Ackley, Schwefel,and Alpine. Preliminary results show that Bat Algorithm and GeneticAlgorithm tend to discover the best solution considering theparameters that have been set up.

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Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420929498 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142092949 ◽

Cited By ~ 1

Author(s):

Fatin Hassan Ajeil ◽

Ibraheem Kasim Ibraheem ◽

Ahmad Taher Azar ◽

Amjad J Humaidi

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Mobile Robot ◽

Particle Swarm ◽

Bat Algorithm ◽

Dynamic Environments ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization ◽

Planning Algorithms

The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.

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