Comparative Research Directions of Population Initialization Techniques using PSO Algorithm

Pervaiz, Sobia; Bangyal, Waqas Haider; Ashraf, Adnan; Nisar, Kashif; Haque, Muhammad Reazul; Ibrahim, Ag Asri Ag; Chowdhry, BS; Rasheed, Waqas; Rodrigues, Joel J. P. C.; Etengu, Richard; Rawat, Danda B.

doi:10.32604/iasc.2022.017304

Cited by 25 publications

(12 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For emergency material scheduling solution, it can be seen from most of the literature that the PSO algorithm has superiority in solving scheduling optimization problems. However, the PSO algorithm has a low fault tolerance, and the optimization results cannot meet the requirements of each distribution, which easily falls into local optimization [23]. Also, PSO algorithms usually construct initial values randomly, so there is an uneven distribution of initial particles, which may lead to uneven solutions [24].…”

Section: Literature Summarymentioning

confidence: 99%

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Efficient emergency material dispatch, amid the aftermath of an emergency event, can help control the spread of the disaster and reduce disaster losses. Herewith, we propose a model with the urgency of material demand as the target coefficient, and the minimum load time and the minimum transportation cost as the total cost. For this model, an improved particle swarm optimization (PSO) algorithm is proposed as the means to optimize the initial positions of particles with good point sets and improve the convergence speed with adaptive dynamic weights to improve the optimization of the emergency material dispatch model. In order to verify the effectiveness of the proposed model and algorithm improvement strategy, the experimental results are verified by means of simulation experiments and algorithm comparison experiments, which show that the proposed emergency material dispatch model and the improved PSO algorithm cannot only solve the post-disaster relief material distribution and dispatching problem but also effectively reduce the total cost of emergency material dispatching.

show abstract

Section: Literature Summarymentioning

confidence: 99%

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…e original SSA uses a random method to generate the initial position of the sparrow population, which is likely to cause uneven distribution of individuals, resulting in poor population diversity. Chaotic motion is characterized by pseudorandomness, ergodicity, and high sensitivity to initial conditions and parameters [35,36].…”

Section: Iterative Chaotic Map Strategymentioning

confidence: 99%

A Multimixed Strategy Improved Sparrow Search Algorithm and Its Application in TSP

Zhang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Aiming at the shortcomings of the sparrow search algorithm (SSA), such as falling into local optimum and slow convergence speed, an improved sparrow search algorithm based on multimixed strategy (MISSA) is proposed in this paper. In the initial stage, the iterative chaotic mapping is used to initialize the population in order to improve the diversity of population. In the foraging stage, the golden sine algorithm and nonlinear convergence factor strategy are introduced to optimize the discoverer-follower model, which make search process more comprehensive and extensive for the discoverer. The elite opposition-based learning strategy is used to update the optimal solution and the population obtained in each iteration to improve the self-learning ability of the algorithm. To verify the rationality of the multimixed strategy selection and efficiency of the proposed algorithm, MISSA is compared with three derived single-strategy improved algorithms, other improved SSAs, and five typical swarm intelligence algorithms using ten basic benchmark functions and CEC 2014 function. The optimization results, diversity analysis, and Wilcoxon rank-sum test results certify that the proposed MISSA has better optimization accuracy, convergence speed, and robustness than other compared methods. Moreover, the practicability and feasibility of MISSA are veriﬁed by solving the traveling salesman problem (TSP).

show abstract

“…In Section 3, the running results of the BPSO algorithm on the simulation of a wall-following robot are analysed and compared with other optimization algorithms in terms of convergence speed, running time, and an optimal solution. Section 4 provides the concluding remarks and prospects (Li et al, 2022;Pervaiz et al, 2022;Wei et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Role of Block Particles Swarm Optimization to Enhance The PID-WFR Algorithm

Suwoyo

Abdurohman

et al. 2022

IJEC

View full text Add to dashboard Cite

In the conventional Proportional Integral Derivation (PID) controller, the parameters are often adjusted according to the formulas and actual application. However, this empirical method will bring two disadvantages. First, testing the program takes much time and usually needs help to reach the optimal solution. Second, the PID parameters will not adapt to the new environment when the situation changes. This paper proposed a method by employing a Block Particles Swarm Optimization (BPSO) to enhance the conventional Proportional Integral Derivation (PID) algorithm to overcome the mentioned disadvantages. The genetic algorithm (GA) first optimized the PID parameters. However, its optimization time is relatively long. Then, a Block Particle Swarm Optimization (BPSO) algorithm is designed to solve the problem of long optimization time. This method was then applied to the wall-following robot problem by realistically simulating it to confirm the performance. After Compared with conventional methods, the proposed method shows a relatively stable solution.

show abstract

Comparative Research Directions of Population Initialization Techniques using PSO Algorithm

Abstract: Likewise, the paper finds the proficiency of numerous quasi-random sequences (QRS) based on initialization approaches by looking at their exhibition analyzed for sixteen notable benchmark test problems.

Cited by 25 publications

References 44 publications

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

A Multimixed Strategy Improved Sparrow Search Algorithm and Its Application in TSP

The Role of Block Particles Swarm Optimization to Enhance The PID-WFR Algorithm

Contact Info

Product

Resources

About