Modified Particle Swarm Optimization With Effective Guides

Karim, Aasam Abdul; Isa, Nor Ashidi Mat; Lim, Wei Hong

doi:10.1109/access.2020.3030950

Cited by 34 publications

(14 citation statements)

References 81 publications

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“…e particle swarm optimization (PSO) algorithm is a population-based random optimization method developed by Kennedy and Eberhart in 1995 [34], inspired by the social behavior of bird overcrowding and fish farming. Of course, this was just the beginning, and extensive research was conducted to improve this method, which led to stronger versions of this method provided by many authors [35][36][37] that the reader could see a summary of the development, improvement, and applications of this algorithm in [38]. To get a proper understanding of this method, consider a group of birds looking for food in an environment.…”

Section: The Related Work and The Classic Psomentioning

confidence: 99%

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Yousefikhoshbakht

2021

Complexity

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The traveling salesman problem (TSP) is one of the most important issues in combinatorial optimization problems that are used in many engineering sciences and has attracted the attention of many scientists and researchers. In this issue, a salesman starts to move from a desired node called warehouse and returns to the starting place after meeting n customers provided that each customer is only met once. The aim of this issue is to determine a cycle with a minimum cost for this salesman. One of the major weaknesses of the PSO algorithm in the classical version is that it gets stuck in local optimizations. Therefore, in the proposed algorithm, called MPSO, the best solution in the current iteration is also used in the movement step. In addition, a variety of local search algorithms are provided that are used when better answers are generated than before. Also, a new method for moving the particle towards the best particle is presented, which, in addition to probably increasing the quality of the new answer, prevents the premature convergence of the algorithm due to consideration of the concept of random. The results evaluated with the results of several metaheuristic algorithms in the literature show the efficiency of the MPSO algorithm because it has been able to achieve excellent solutions in most of these instances.

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Section: The Related Work and The Classic Psomentioning

confidence: 99%

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Yousefikhoshbakht

2021

Complexity

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“…The proposed MAIA is compared with three other stateof-the-art ABC algorithms, i.e., DGABC [48], DFSABC- elite [49], GRABC [50] and three other advanced heuristic intelligent algorithms: RIPGA [39], MPSOEG [51], DE-MOIA [52]. Tab.…”

Section: Parameter Settings Of Algorithm Simulationmentioning

confidence: 99%

Parameter Estimation for a Capacitive Coupling Communication Channel Within a Metal Cabinet Based on a Modified Artificial Immune Algorithm

Guo

Wang

et al. 2021

IEEE Access

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The channel model optimization algorithm plays a critical role in novel communication method research. Wireless sensor connections using capacitive coupling communication inside a metal cabinet such as spacecraft are an emerging communication technology. However, channel modeling along with optimization methods have not been systematically investigated. In this paper, a modified artificial immune algorithm (MAIA) was developed to optimize a few tens of model parameters for the capacitive coupling communication channel within a metal cabinet. The mathematical model of the communication channel was derived from the equivalent circuit model by analyzing the capacitive coupling electric field distribution. Unknown parameters in the model were optimally estimated by adopting MAIA with the objective of minimizing the root mean square error (RMSE) between the model computed data and simulation or experimental data. The proposed scheme enhanced the convergence performance by incorporating the artificial bee colony (ABC) algorithm, modifying the strategies of immune operations and introducing a similarity detection step. Validation results showed that the frequency response of the optimized model matched well with the simulation and experimental data, verifying the feasibility and robustness of the proposed MAIA. Compared with three other state-of-the-art ABC algorithms and three enhanced intelligent algorithms, it was demonstrated that the proposed algorithm performed better with respect to convergence speed and accuracy. The study provided a multiparameter channel model estimation solution for capacitive coupling communication within a metal cabinet research.

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“…Since the particle swarm optimization algorithm (PSO) was proposed by Kennedy and Eberhart in 1995 [1], it has obtained great achievements in finding the optimal value of continuous nonlinear equations [2]. PSO is a special branch of evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Zhang

Sheng

et al. 2021

Computational Intelligence and Neuroscience

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The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

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Modified Particle Swarm Optimization With Effective Guides

Cited by 34 publications

References 81 publications

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Parameter Estimation for a Capacitive Coupling Communication Channel Within a Metal Cabinet Based on a Modified Artificial Immune Algorithm

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

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