Camel Herds Algorithm: a New Swarm Intelligent Algorithm to Solve Optimization Problems

Al-Obaidi, Ahmed T. Sadiq; Abdullah, Hasanen S.; Ahmed, Zied O.

doi:10.31436/ijpcc.v3i1.44

Cited by 14 publications

(5 citation statements)

References 6 publications

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“…Additionally, the results indicate that the average relative error (ARE) of the proposed MCA-HS is less than its corresponding of the standalone MCA. Furthermore, the results achieved from the proposed MCA-HS approach are compared with some other meta-heuristic methods that proposed in [14], [24]- [26], as shown in Table 3. The RE in the makespan time of the resulted solutions from each instance of the suggested meta-heuristics in [12], [24]- [26] is computed using (3).…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the results achieved from the proposed MCA-HS approach are compared with some other meta-heuristic methods that proposed in [14], [24]- [26], as shown in Table 3. The RE in the makespan time of the resulted solutions from each instance of the suggested meta-heuristics in [12], [24]- [26] is computed using (3). Our proposed algorithm (MCA-HS) has an ARE greater than the (AFSA-HS, AFSA-VND and CS-ILF) algorithms, but (MCA-HS) has an ARE less than the CHA.…”

Section: Resultsmentioning

confidence: 99%

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Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm

Jawad

Younis

Sadiq

2022

IJ-AI

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The classical job shop scheduling (JSS) problem can be extended by allowing processing of an operation by any machine from a given set. This type of scheduling is known as flexible job shop scheduling (FJSS) problem. It incorporates all the difficulties and complexities of its predecessor classical problem. However, it is more complex as it is required to determine the assignment of operations to the machine. Swarm intelligence techniques proved their effectiveness in solving a wide range of complex NP-Hard real world problems. One of these techniques is the meerkat clan algorithm (MCA) that has been successfully applied to various optimization problems. This paper presents a modified MCA for solving the FJSS problem. The modification is based on using harmony search (HS). The introduction of HS provides more exploitation and intensification. HS generates various solutions, which are provided to the MCA. As a result, the exploitation of the local optimum is increased, which in turn increases the convergence rate. The experimental results show that the improved method achieves higher quality schedules. Additionally, the convergence rate is speeded up compared with the standalone algorithm. This gives the proposed method the superiority over the original algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm

Jawad

Younis

Sadiq

2022

IJ-AI

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“…Once the other member of the caravan finds another location of the food, it communicates with the other caravan members to change their path toward the new oasis. Several researchers have inspired camel behavior as a new optimization algorithm for finding an optimal solution for practical problems, such as designing PID controllers [15], [16]. At the beginning of the searching mission, the camels are randomly spread out in the desert to determine the supply (water and food).…”

Section: Camel Optimization Algorithm (Coa)mentioning

confidence: 99%

Control System Design for Azimuth Position of Earth Station Antennas

Al-Mayyahi,

Aldair,

Khalaf

2024

Preprint

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Smart earth station antennas have been used for several decades in many applications, from satellite communications to space object detection and tracking. The accuracy of the azimuth position for such antennas plays a crucial role in most steerable ground station antennas. Satellite tracking and space object detection demand precise tracking capabilities from the Earth. Several methods and techniques have been developed and used in industry to control the directions of ground station antennas, including the azimuth position. The challenge of azimuth tracking is increasing with the demand for full-sky coverage and with the exponential increase in space objects, including man-made satellites and operational and nonoperational objects; thus, providing accurate tracking is a key technology that demands continuous enhancement and development. This article presents the use of a PID-proportional-integral-derivative controller, a slide mode controller and a fractional order PID controller. It also introduces a new methodology based on model predictive control (MPC). The manuscript provides the core design for each of these controllers and provides insight into the performance of each controller even in the presence of disturbance. The camel optimization algorithm (COA) was used to obtain the optimal design parameters of each controller in the considered scenarios.

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“…The camel herds algorithm (CHA) is a metaheuristic SI optimization algorithm and intelligent multi-agent system proposed by Ahmed [7]. The CHA is inspired by the behavior of camels and how they search for food in their desert environment.…”

Section: Camel Herds Algorithm: a Brief Overviewmentioning

confidence: 99%

“…Our algorithm is an improvement on the Camel Herds Algorithm (CHA). Which is derived from the way camel herds search for food in their environment and from their ability to sense the humidity in the air to reach water [7].…”

Section: Introductionmentioning

confidence: 99%

Solving Routing Problem Using Improved Camel Herds Algorithm

Ardabili¹,

Ahmed²,

Abbood

2020

IJPCC

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This paper introduces a new adaptive, distributed routing algorithm based on the Improved Camel Herds Algorithm (CHA). It is an intelligent, multi-agent optimization algorithm that is inspired by the behavior of camels and how they search for food in their desert environment. We examine its ability to solve the routing problem in switched networks: finding the shortest path in the process of transferring data packets between networks. Many meta-heuristic algorithms have been previously proposed to address the routing problem, and this proposed approach is compared with three well-known algorithms (ACO, GA, PSO) on ten graphs (weighted, integer, and not negative) and datasets with various size of nodes (from 10 nodes to 297 nodes). Three performance criteria were used to evaluate the performance of the algorithms (mean relative error, standard deviation, and number of function evaluations). The results proved that the performance of the proposed algorithm is both promising and competitive with other algorithms.

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Camel Herds Algorithm: a New Swarm Intelligent Algorithm to Solve Optimization Problems

Cited by 14 publications

References 6 publications

Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm

Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm

Control System Design for Azimuth Position of Earth Station Antennas

Solving Routing Problem Using Improved Camel Herds Algorithm

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