Assessment of evolutionary programming, firefly algorithm and cuckoo search algorithm in single-objective optimization

2016 ◽  
Author(s):  
Muhammad Zakyizzuddin Rosselan ◽  
Shahril Irwan Sulaiman
Keyword(s):  
Firefly Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Evolutionary Programming ◽  
Cuckoo Search Algorithm ◽  
Single Objective

scholarly journals Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Muhammad Zakyizzuddin Bin Rosselan ◽  
Shahril Irwan Bin Sulaiman ◽  
Norhalida Othman
Keyword(s):  
Evolutionary Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Evolutionary Programming ◽  
Cuckoo Search Algorithm ◽  
Computational Time ◽  
Fitness Value ◽  
Single Objective

In this study proposes an evaluation of different computational intelligences, i.e Fast-Evolutionary Algorithm (FEP), Firefly Algorithm (FA) and Mutate-Cuckoo Search Algorithm (MCSA) for solving single-objective optimization problem. FEP and MCSA are based on the conventional Evolutionary Programming (EP) and Cuckoo Search Algorithm (CSA) with modifications and adjustment to boost up their search ability. In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. The results showed that MCSA is better compare with FEP and FA in term of fitness value while FEP is fastest algorithm in term of computational time compare with other two algorithms.

Multi-Objective Optimal Power Flow Using Metaheuristic Optimization Algorithms With Unified Power Flow Controller to Enhance the Power System Performance

2018 ◽  
pp. 1-33
Author(s):  
G. V. Nagesh Kumar ◽  
B. Venkateswara Rao ◽  
D. Deepak Chowdary ◽  
Polamraju V. S. Sobhan
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Unified Power Flow Controller ◽  
Multi Objective ◽  
Optimal Power

In this chapter a multi objective optimal power flow (OPF) is obtained by using latest Metaheuristic optimization techniques BAT search algorithm (BAT), cuckoo search algorithm (CSA) and firefly algorithm (FA) with Unified power flow controller (UPFC). UPFC is a voltage source converter type Flexible Alternating Current Transmission System (FACTS) device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. To enhance the power system performance, the optimal power flow has been incorporated UPFC along with BAT algorithm, cuckoo search algorithm and firefly algorithm based multi objective function comprising of two objectives those are total real power loss and the fuel cost of total real power generation. The BAT algorithm, cuckoo search algorithm and firefly algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm, cuckoo search algorithm and firefly algorithms are compared with Differential Evaluation (DE).

A new hybrid cuckoo search and firefly optimization

2018 ◽  
Vol 24 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Mariam Elkhechafi ◽  
Hanaa Hachimi ◽  
Youssfi Elkettani
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Firefly Optimization ◽  
New Generation

Abstract In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms (CS-FA).

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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