scholarly journals Meerkat Clan Algorithm for Solving N-Queen Problems

2021 ◽  
pp. 2082-2089
Author(s):  
Sura Mazin Ali ◽  
Noor Thamer Mahmood ◽  
Samer Amil Yousif
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Combinatorial Problem ◽  
Search Space ◽  
Problem Solution ◽  
Problem Complexity ◽  
Heuristic Function ◽  
Swarm Optimization ◽  
Large N ◽  
Np Problems

The swarm intelligence and evolutionary methods are commonly utilized by researchers in solving the difficult combinatorial and Non-Deterministic Polynomial (NP) problems. The N-Queen problem can be defined as a combinatorial problem that became intractable for the large ‘n’ values and, thereby, it is placed in the NP class of problems. In the present study, a solution is suggested for the N-Queen problem, on the basis of the Meerkat Clan Algorithm (MCA). The problem of n-Queen can be mainly defined as one of the generalized 8-Queen problem forms, for which the aim is placing 8 queens in a way that none of the queens has the ability of killing the others with the use of the standard moves of the chess queen. The Meerkat Clan environment is a directed graph, called the search space, produced for the efficient search of valid n-queens’ placement, in a way that they do not cause harm to one another. This paper also presents the development of an intelligent heuristic function which is helpful to find the solution with high speed and effectiveness. This study includes a detailed discussion of the problem background, problem complexity, Meerkat Clan Algorithm, and comparisons of the problem solution with the Practical Swarm Optimization (PSO) and Genetic Algorithm (GA. It is an entirely review-based work which implemented the suggested designs and architectures of the methods and a fair amount of experimental results.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

scholarly journals A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Tao Sun ◽  
Ming-hai Xu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Movement ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Dimension Functions ◽  
Solution Accuracy ◽  
Crossover And Mutation

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

scholarly journals Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingming Wang ◽  
Li Wang ◽  
Xinyue Xu ◽  
Yong Qin ◽  
Lingqiao Qin
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
High Speed ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
High Speed Railway ◽  
Service Strategy ◽  
Delay Propagation

In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.

Genetic algorithm search space splicing particle swarm optimization as general-purpose optimizer

2013 ◽  
Vol 128 ◽  
pp. 153-159 ◽  
Author(s):  
Hao Li ◽  
Chanin Nantasenamat ◽  
Teerawat Monnor ◽  
Chartchalerm Isarankura-Na-Ayudhya ◽  
Virapong Prachayasittikul
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Search Space ◽  
General Purpose ◽  
Swarm Optimization ◽  
Algorithm Search

Evaluating Process Parameters of Multi-Pass Turning Process Using Hybrid Genetic Simulated Swarm Algorithm

2015 ◽  
Vol 14 (04) ◽  
pp. 215-233 ◽  
Author(s):  
R. Gayatri ◽  
N. Baskar
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Evolutionary Algorithms ◽  
Process Parameters ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Turning Process ◽  
Swarm Optimization

Evolutionary computation is one of the important problems solving method frequently used by the researchers. The choice of an algorithm to optimize the problem is determined by some sort of reliability of the researcher with that technique. To overcome the limitations in individual algorithms and to achieve synergic effects, fusion or hybridization of two or more algorithms is carried out. Hybrid algorithms have gained popularity because there is no evidence that a universal optimal strategy exists for solving optimization problems. In this work, a hybrid algorithm called hybrid genetic simulated swarm (HGSS) algorithm is proposed to optimize the parameters of multi-pass turning operation. The HGSS algorithm is a fusion of genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms. The objectives of this work are (i) to explore and exploit the problem search space through hybridization, (ii) to justify that proficient hybridization of evolutionary algorithms (EAs) will yield an efficient means to solve the optimization problems. In this work, the EAs such as GA, SA and PSO are also applied to optimize parameters and results are compared with HGSS. The results of the proposed work HGSS are very effective than other algorithms.

scholarly journals Discrete Chicken Swarm Optimization for the Quadratic Assignment Problem

2018 ◽  
Vol 11 (3) ◽  
pp. 925
Author(s):  
Soukaina Cherif Bourki Semlali ◽  
Mohammed Essaid Riffi ◽  
Fayçal Chebihi
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Combinatorial Problem ◽  
Quadratic Assignment ◽  
Swarm Optimization ◽  
Average Percentage ◽  
Local Search Strategy ◽  
Chicken Swarm Optimization

<p>The main objective of our research is to improve an adaptation of the chicken swarm optimization algorithm (CSO) to solve the quadratic assignment problem, which is a well-known combinatorial optimization problem. The new approach is based on the CSO without using a local search, the CSO-QAP is a stochastic method inspired from the behavior of chickens in swarm while searching for food. The experiments are performed on a set of 56 benchmark QAPLIB instances. To prove the robustness of our algorithm a comparative analysis is done with the known metaheuristic of Genetic algorithm based on SCX. The average percentage of error to get the best Known solution in our proposed work with the results obtained by applying a simple genetic algorithm using sequential constructive crossover for the quadratic assignment problem. The results show the effectiveness of the proposed CSO-QAP to solve the Quadratic assignment problem in term of time and quality of solutions. The proposed adaptation can be further applied by using a local search strategy to solve the same problem or another combinatorial problem.</p>

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