Improved discrete plant propagation algorithm for solving the traveling salesman problem

Almazini, Hussein Fouad; Mortada, Salah; Almazini, Hassan; Al-Behadili, Hayder Naser Khraibet; Alkenani, Jawad

doi:10.11591/ijai.v11.i1.pp13-22

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…There are two types of metaheuristic optimization approaches: trajectory-based and population-based methods [31][32][33]. The number of tentative answers employed in each stage of the (iterative) algorithm is the fundamental distinction between these two classes.…”

Section: Metaheuristic Optimization Methodsmentioning

confidence: 99%

A review: On bio-inspired optimization methods for path planning of mobile robot

Hason

Al-Darraji

2023

IJICI

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In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

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Section: Metaheuristic Optimization Methodsmentioning

confidence: 99%

A review: On bio-inspired optimization methods for path planning of mobile robot

Hason

Al-Darraji

2023

IJICI

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“…They stated that the experimental results show that the proposed method is stable and superior to the compared algorithms. Almazini et al [40] solved the TSP using the plant propagation algorithm (PPA) due to the inadequacy of traditional algorithms. However, they stated that the basic version of this method was insufficient in solution quality and proposed PPGA by making improvements such as crossover and mutation on the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Approach Based on Collective Intelligence to Solve Traveling Salesman Problems

Kiran,

Beskirli

2024

Biomimetics

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This paper presents a novel approach based on the ant system algorithm for solving discrete optimization problems. The proposed method is based on path construction, path improvement techniques, and the footprint mechanism. Some information about the optimization problem and collective intelligence is used in order to create solutions in the path construction phase. In the path improvement phase, neighborhood operations are applied to the solution, which is the best of the population and is obtained from the path construction phase. The collective intelligence in the path construction phase is based on a footprint mechanism, and more footprints on the arc improve the selection chance of this arc. A selection probability is also balanced by using information about the problem (e.g., the distance between nodes for a traveling salesman problem). The performance of the proposed method has been investigated on 25 traveling salesman problems and compared with state-of-the-art algorithms. The experimental comparisons show that the proposed method produced comparable results for the problems dealt with in this study.

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“…Figure 1. An example of VRPTW Metaheuristics algorithms can be divided into local search and population-based techniques [13], [14]. The local search technique manipulates a single solution by exchanging segments of its components to produce better solutions, whereas the population-based technique uses more than one solution.…”

Section: Introductionmentioning

confidence: 99%

An improved artificial bee colony with perturbation operators in scout bees’ phase for solving vehicle routing problem with time windows

Mortada

Yusof

2023

IJ-AI

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>An example of a combinatorial problem is the vehicle routing problem with time windows (VRPTW), which focuses on choosing routes for a limited number of vehicles to serve a group of customers in a restricted period. Meta-heuristics algorithms are successful techniques for VRPTW, and in this study, existing modified artificial bee colony (MABC) algorithm is revised to provide an improved solution. One of the drawbacks of the MABC algorithm is its inability to execute wide exploration. A new solution that is produced randomly and being swapped with best solution when the previous solution can no longer be improved is prone to be trapped in local optima. Hence, this study proposes a perturbed MABC known as pertubated (P-MABC) that addresses the problem of local optima. P-MABC deploys five types of perturbation operators where it improvises abandoned solutions by changing customers in the solution. Experimental results show that the proposed P-MABC algorithm requires fewer number of vehicles and least amount of travelled distance compared with MABC. The P-MABC algorithm can be used to improve the search process of other population algorithms and can be applied in solving VRPTW in domain applications such as food distribution.</span></p></div></div></div>

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Improved discrete plant propagation algorithm for solving the traveling salesman problem

Cited by 4 publications

References 35 publications

A review: On bio-inspired optimization methods for path planning of mobile robot

A review: On bio-inspired optimization methods for path planning of mobile robot

A New Approach Based on Collective Intelligence to Solve Traveling Salesman Problems

An improved artificial bee colony with perturbation operators in scout bees’ phase for solving vehicle routing problem with time windows

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