Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

Qamar, Muhammad Salman; Tu, Shanshan; Ali, Farman; Armghan, Ammar; Munir, Muhammad Fahad; Alenezi, Fayadh; Muhammad, Fazal; Ali, Asar; Alnaim, Norah

doi:10.3390/app11114780

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…Different research papers have been published on the improve performance of SNs in WSNs. To take into account the transmission distance, transmission direction, and the role of ants in the search process as a whole, a method termed improved ACO-based routing is proposed in [9,10]. An example of a routing strategy based on clusters that makes use of type-2 fuzzy logic and an ACO algorithm can be found in [11].…”

Section: Methodsmentioning

confidence: 99%

Wireless Sensors Networks Energy Optimization using LEACH and ANNs

Qamar¹,

Haq²

2023

Preprint

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Applications for Wireless Sensor Networks (WSNs) range from monitoring the environment to automating factories. However, sustained and effective functioning is made more difficult by Sensor Nodes (SNs) limited energy supplies in which optimization is the main issue. So with the aim of increasing the lifespan by decreasing the energy consumption of WSN, Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with Deep Learning (DL) algorithm is analyzed in this paper. LEACH is a hierarchical mechanism that elects Cluster Heads (CHs) and regularly rotates their positions in order to allocate energy use effectively by using the same amount of energy. However, Deep Learning (DL) method is used to further improve energy optimization. In many applications, the types of Deep Learning methods like Artificial Neural Networks (ANNs) have shown to be very useful. Using this method, WSNs may make more efficient decisions that reduce energy consumption. Data aggregation, duty cycling, and transmission protocols may all be optimized by Deep Learning model's ability to recognize patterns and forecast network behavior. This results in lower energy consumption, a longer lifespan for the network, and better overall performance.

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Section: Methodsmentioning

confidence: 99%

Wireless Sensors Networks Energy Optimization using LEACH and ANNs

Qamar¹,

Haq²

2023

Preprint

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“…The TSP is widely studied and is perhaps the most common combinatorial optimization problem in experimental studies. It has been used in machine learning [34,35], ant colony optimization [36,37], GA [19,24,38,39], other forms of EA [22,23,40], and other metaheuristics [41][42][43][44][45]. There are variations of the problem, such as the Asymmetric TSP (ATSP), where the cost of using an edge differs depending upon the direction of travel along the edge [46,47].…”

Section: Tspmentioning

confidence: 99%

Cycle Mutation: Evolving Permutations via Cycle Induction

Cicirello

2022

Applied Sciences

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Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems such as the traveling salesperson problem (TSP), as well as assignment problems such as the quadratic assignment problem (QAP) and largest common subgraph (LCS). We propose cycle mutation, a new mutation operator whose inspiration is the well-known cycle crossover operator, and the concept of a permutation cycle. We use fitness landscape analysis to explore the problem characteristics for which cycle mutation works best. As a prerequisite, we develop new permutation distance measures: cycle distance, k-cycle distance, and cycle edit distance. The fitness landscape analysis predicts that cycle mutation is better suited for assignment and mapping problems than it is for ordering problems. We experimentally validate these findings showing cycle mutation’s strengths on problems such as QAP and LCS, and its limitations on problems such as the TSP, while also showing that it is less prone to local optima than commonly used alternatives. We integrate cycle mutation into the open source Chips-n-Salsa library, and the new distance metrics into the open source JavaPermutationTools library.

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“…Lingxia Liao et al [22] aimed to solve a generic controller placement problem (GCP) by planning the placement of controllers over SDN systems; to achieve this, they proposed a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant of the PSO algorithm. Muhammad Salman Qamar et al [23] aimed to settle the traveling salesman problem (TSP) by proposing a novel best-worst ant system (BWAS) based on the PSO algorithm. Xianjia Wang et al [24] investigated the role of the particle swarm optimization (PSO) strategy update rules in the evolution of cooperation in the prisoner's dilemma (PD) on scale-free networks.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Chen

Xiao

2021

Sensors

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An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

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Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

Cited by 14 publications

References 30 publications

Wireless Sensors Networks Energy Optimization using LEACH and ANNs

Wireless Sensors Networks Energy Optimization using LEACH and ANNs

Cycle Mutation: Evolving Permutations via Cycle Induction

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

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