scholarly journals GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guoming Du ◽  
Yangbo Chen ◽  
Wei Sun
Keyword(s):  
Optimization Problems ◽  
Bat Algorithm ◽  
Population Diversity ◽  
Premature Convergence ◽  
Local Optimum ◽  
Guangzhou City ◽  
Location Allocation ◽  
Geographic Space ◽  
Spatial Search ◽  
Gis Technique

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

scholarly journals Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 884
Author(s):  
Petr Stodola ◽  
Karel Michenka ◽  
Jan Nohel ◽  
Marian Rybanský
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Use Of Knowledge

The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

scholarly journals Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xingwang Huang ◽  
Chaopeng Li ◽  
Yunming Pu ◽  
Bingyan He
Keyword(s):  
Probability Distribution ◽  
Bat Algorithm ◽  
Random Coefficients ◽  
Function Optimization ◽  
Premature Convergence ◽  
Local Optimum ◽  
Search Range ◽  
Numerical Function ◽  
Numerical Function Optimization ◽  
Gaussian Probability Distribution

Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.

scholarly journals An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1637
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Ali Fatahi ◽  
Hoda Zamani ◽  
Seyedali Mirjalili ◽  
Laith Abualigah
Keyword(s):  
Mechanical Engineering ◽  
Optimization Problems ◽  
Population Diversity ◽  
Local Optimum ◽  
Friedman Test ◽  
Exploration And Exploitation ◽  
Adaptation Mechanism ◽  
Local Optima ◽  
Engineering Problems ◽  
Moth Flame Optimization Algorithm

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

scholarly journals A Tent Marine Predators Algorithm with Estimation Distribution Algorithm and Gaussian Random Walk for Continuous Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chang-Jian Sun ◽  
Fang Gao
Keyword(s):  
Random Walk ◽  
Population Distribution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Population Diversity ◽  
Initial Population ◽  
Local Optimum ◽  
Marine Predators ◽  
Gaussian Random Walk ◽  
Estimation Distribution Algorithm

The marine predators algorithm (MPA) is a novel population-based optimization method that has been widely used in real-world optimization applications. However, MPA can easily fall into a local optimum because of the lack of population diversity in the late stage of optimization. To overcome this shortcoming, this paper proposes an MPA variant with a hybrid estimation distribution algorithm (EDA) and a Gaussian random walk strategy, namely, HEGMPA. The initial population is constructed using cubic mapping to enhance the diversity of individuals in the population. Then, EDA is adapted into MPA to modify the evolutionary direction using the population distribution information, thus improving the convergence performance of the algorithm. In addition, a Gaussian random walk strategy with medium solution is used to help the algorithm get rid of stagnation. The proposed algorithm is verified by simulation using the CEC2014 test suite. Simulation results show that the performance of HEGMPA is more competitive than other comparative algorithms, with significant improvements in terms of convergence accuracy and convergence speed.

Comparison of Different Bat Initialization Techniques for Global Optimization Problems

2021 ◽  
Vol 12 (1) ◽  
pp. 157-184
Author(s):  
Wasqas Haider Bangyal ◽  
Jamil Ahmad ◽  
Hafiz Tayyab Rauf
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Search Space ◽  
Research Problem ◽  
Population Based ◽  
Premature Convergence ◽  
Ongoing Research ◽  
Low Discrepancy Sequences ◽  
Stochastic Search Technique

Bat algorithm (BA) is a population-based stochastic search technique that has been widely used to solve the diverse kind of optimization problems. Population initialization is the current ongoing research problem in evolutionary computing algorithms. Appropriate population initialization assists the algorithm to investigate the swarm search space effectively. BA faces premature convergence problem to find actual global optimization value. Low discrepancy sequences are slightly lesser random number than pseudo-random; however, they are more powerful for computational approaches. In this work, new population initialization approach Halton (BA-HA), Sobol (BA-SO), and Torus (BA-TO) are proposed, which helps bats to avoid from the premature convergence. The proposed approaches are examined on standard benchmark functions, and simulation results are compared with standard BA initialized with uniform distribution. The results depict that substantial enhancement can be attained in the performance of standard BA while varying the random numbers sequences to low discrepancy sequences.

A Diversity Guided Particles Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1429-1433
Author(s):  
Na Li ◽  
Yuan Xiang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Population Diversity ◽  
Premature Convergence ◽  
Local Optimum ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Search Capability

A new particle swarm optimization algorithm (a diversity guided particles swarm Optimization), which is guided by population diversity, is proposed. In order to overcome the premature convergence of the algorithm, a metric to measure the swarm diversity is designed, the update of velocity and position of particles is controlled by this criteria, and the four sub-processes are introduced in the process of updating in order to increase the swarm diversity, which enhance to the ability of particle swarm optimization algorithm (PSO) to break away from the local optimum. The experimental results exhibit that the new algorithm not only has great advantage of global search capability, but also can avoid the premature convergence problem effectively.

scholarly journals Chicken Swarm Optimization Based on Elite Opposition-Based Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Chiwen Qu ◽  
Shi’an Zhao ◽  
Yanming Fu ◽  
Wei He
Keyword(s):  
Optimization Problems ◽  
Local Development ◽  
Random Search ◽  
Standard Test ◽  
Population Diversity ◽  
Local Optimum ◽  
Basic Algorithm ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Chicken Swarm Optimization

Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.

Hybrid Differential Evolutionary Algorithm Based on Extremal Optimization

2013 ◽  
Vol 339 ◽  
pp. 259-264 ◽  
Author(s):  
Cong Jiao Wang ◽  
Xi Huai Wang ◽  
Jian Mei Xiao
Keyword(s):  
Differential Evolution ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Iteration Process ◽  
Population Based ◽  
Premature Convergence ◽  
Local Optimum ◽  
Extremal Optimization ◽  
Differential Evolutionary Algorithm ◽  
High Degree

A new hybrid algorithm based on Differential Evolution (DE) and Extremal Optimization (EO) is proposed to solve the premature convergence and low precision of standard differential evolution when applied to complex optimization problems. The key points of it lie in: the hybrid algorithm introduced the population-based Extremal Optimization algorithm in the iteration process of DE when population aggregation got the high degree, which uses the volatility of EO to increase the diversity of population and the ability of breaking away from the local optimum. Simulations show that the hybrid algorithm has remarkable global convergence ability, and can avoid the premature convergence effectively.

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