Migration-Based Moth-Flame Optimization Algorithm

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

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

Entropy ◽

10.3390/e23121637 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1934 ◽

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.

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Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

Critical Developments and Applications of Swarm Intelligence - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5134-8.ch002 ◽

2018 ◽

pp. 27-52 ◽

Cited By ~ 2

Author(s):

Jiarui Zhou ◽

Junshan Yang ◽

Ling Lin ◽

Zexuan Zhu ◽

Zhen Ji

Keyword(s):

Particle Swarm Optimization ◽

High Efficiency ◽

Particle Swarm ◽

Global Optimum ◽

Population Diversity ◽

Local Optimum ◽

Swarm Optimization ◽

Local Optima ◽

Von Neumann ◽

Swarm Intelligence Algorithm

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. It is easy to get trapped in local optima. For this reason, improvements are made to detect stagnation during the optimization and reactivate the swarm to search towards the global optimum. This chapter imposes the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel crown jewel defense (CJD) strategy is introduced to restart the swarm when it is trapped in a local optimum region. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting. Experimental results suggest that the LCJDPSO-rfl outperforms state-of-the-art PSO variants on most of the functions.

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

Entropy ◽

10.3390/e22080884 ◽

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).

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

Mathematical Problems in Engineering ◽

10.1155/2020/2843436 ◽

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.

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A simple diversity guided firefly algorithm

Kybernetes ◽

10.1108/k-11-2013-0241 ◽

2015 ◽

Vol 44 (1) ◽

pp. 43-56 ◽

Cited By ~ 2

Author(s):

Shuhao Yu ◽

Shoubao Su ◽

Li Huang

Keyword(s):

Design Methodology ◽

Firefly Algorithm ◽

Threshold Value ◽

Population Diversity ◽

Local Optimum ◽

Content Type ◽

Different Dimensions ◽

The Given ◽

Modified Equation ◽

Modified Firefly Algorithm

Purpose – The purpose of this paper is to present a modified firefly algorithm (FA) considering the population diversity to avoid local optimum and improve the algorithm’s precision. Design/methodology/approach – When the population diversity is below the given threshold value, the fireflies’ positions update according to the modified equation which can dynamically adjust the fireflies’ exploring and exploiting ability. Findings – A novel metaheuristic algorithm called FA has emerged. It is inspired by the flashing behavior of fireflies. In basic FA, randomly generated solutions will be considered as fireflies, and brightness is associated with the objective function to be optimized. However, during the optimization process, the fireflies become more and more similar and gather into the neighborhood of the best firefly in the population, which may make the algorithm prematurely converged around the local solution. Research limitations/implications – Due to different dimensions and different ranges, the population diversity is different undoubtedly. And how to determine the diversity threshold value is still required to be further researched. Originality/value – This paper presents a modified FA which uses a diversity threshold value to guide the algorithm to alternate between exploring and exploiting behavior. Experiments on 17 benchmark functions show that the proposed algorithm can improve the performance of the basic FA.

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Cooperative Artificial Fish Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.741.359 ◽

2015 ◽

Vol 741 ◽

pp. 359-362

Author(s):

Hong Wei Zhao ◽

Li Wei Tian

Keyword(s):

Data Clustering ◽

Optimization Problems ◽

Swarm Optimization ◽

Benchmark Data ◽

Artificial Fish Swarm ◽

New Type ◽

Complex Optimization ◽

Comparative Results ◽

Swarm Intelligence Algorithm ◽

Very High

Basic Artificial Fish Swarm(AFS) Algorithm is a new type of heuristic swarm intelligence algorithm but optimization is difficult to get a very high precision due to the randomness of the artificial fish behavior.This paper presents an extended AFS algorithm, namely the Cooperative Artificial Fish Swarm (CAFS),which significantly improves the original AFS in solving complex optimization problems. In this work,firstly,CAFS algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by CAFS, Particle Swarm Optimization (PSO) are studied.Secondly,K-medoids and CAFS algorithm is used for data clustering on several benchmark data sets.The simulation results show that the proposed CAFS outperforms the other two algorithms in terms of accuracy,robustness and convergence speed.

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An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

Mathematical Problems in Engineering ◽

10.1155/2013/413565 ◽

2013 ◽

Vol 2013 ◽

pp. 1-29 ◽

Cited By ~ 20

Author(s):

Shouheng Tuo ◽

Longquan Yong ◽

Tao Zhou

Keyword(s):

High Dimension ◽

Learning Strategy ◽

Optimization Problems ◽

Search Algorithm ◽

Harmony Search ◽

Population Based ◽

Population Diversity ◽

Random Mutation ◽

Complex Optimization ◽

Teaching Learning

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

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Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem

Modern Applied Science ◽

10.5539/mas.v12n11p366 ◽

2018 ◽

Vol 12 (11) ◽

pp. 366 ◽

Cited By ~ 1

Author(s):

Issam AlHadid ◽

Khalid Kaabneh ◽

Hassan Tarawneh

Keyword(s):

Simulated Annealing ◽

Optimization Problems ◽

Search Space ◽

Local Optimum ◽

Solution Quality ◽

University Course Timetabling ◽

Hybrid Simulated Annealing ◽

Complex Optimization ◽

List Memory ◽

University Course

Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the literature when tested on ITC2007-Track3 university course timetabling datasets.

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

Computational Intelligence and Neuroscience ◽

10.1155/2021/7695596 ◽

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.

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