Analysis and comparison among Ant System; Ant Colony System and Max-Min Ant System with different parameters setting

Jangra, Renu; Kait, Ramesh

doi:10.1109/ciact.2017.7977376

Cited by 25 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model hybrids ABC [57] aABC [43,58], adaptive ABC (AABC) [59], vortex search [60], cooperative ABC (CABC) [61,62], cooperative micro-ABC (CMABC) [63], interval cooperative multiobjective ABC (ICMOABC) [62], ABC-PSO [64], multiobjective directed bee colony optimization (MODBCO) [65], Scoutless ABC [35], directed ABC [66,67] ACO [68] ACOR [36], heuristic-PS-ACO (HPSACO) [69], hybrid ACO [70], ACO-PSO [71], PS-ACO [72], ACO-SA [73], MWIS-ACO-LS [74], hybrid ACO (HAntCO) [75], min-max ant System (MMAS) [72,76], GA-ACO-SA [77], self-adaptive ant colonygenetic hybrid [78], GA-ACO [79], ACS [80], greedy ACS [81] BA [82] Binary BA [83], hybrid BA with ABC [84], BA-HS [85], adaptive BA [86], adaptive multiswarm BA (AMBA) [87], binary BA [83], differential operator & Levy flights BA [87], directed artificial BA (DABA) [88], double-subpopulation Levy flight BA (DLBA) [89], dynamic virtual BA (DVBA) [90], improved DVBA with probabilistic selection [91], island multipopulational parallel BA (IBA)…”

Section: Modelmentioning

confidence: 99%

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

Chopra

Dhiman

Sharma

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

show abstract

Section: Modelmentioning

confidence: 99%

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

Chopra

Dhiman

Sharma

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Dorigo and Gambardella reported that the ACS algorithm could produce a 2.2% better path length than the genetic algorithm (GA), 1.3% better than evolutionary programming (EP), and 7.8% better than simulated annealing (SA) [23,24]. Because the ACS algorithm is better than the ACO in terms of the cost of the route generated, and the computational time required in literature [25], Terefore, this paper uses ACS algorithm as the basis for improvement. In ACS algorithm the foraging behaviour of ants in nature is simulated, and certain intelligence is added to ants which is mainly achieved by adding a certain memory to the ants and processing the unique pheromone of ants-pheromone volatilization.…”

Section: Related Wordsmentioning

confidence: 99%

Robot Path Planning of Improved Adaptive Ant Colony System Algorithm Based on Dijkstra

Feng

Guo-zhan

et al. 2022

Journal of Robotics

View full text Add to dashboard Cite

Path planning is one of the key technologies of robot. Aiming at the problems of slow convergence speed and inefficient search of traditional Ant Colony System Algorithm, an adaptive Ant Colony System Algorithm based on Dijkstra is proposed in the paper. Firstly, Dijkstra algorithm is applied to searching the initial path in the grid environment, constructing the initial path, optimizing the initial pheromone in the region, therefore, the Ant Colony System Algorithm avoid falling into blind search in the initial stage; In the transition probability, the disguised angle probability function and parameter adaptive pseudo-random proportion rule are introduced to improve the search efficiency and convergence speed of the algorithm, and eliminate the inferior ant path; Finally, B-spline interpolation curve is used to smooth the path. Compared with the traditional Ant Colony System Algorithm, the simulation results in the grid environment demonstrating its effectiveness to improve convergence speed and to enhance search efficiency are provided. The characteristics of the improved Ant Colony System Algorithm are faster convergence speed and better planning.

show abstract

“…The simulation was performed to test the proposed method and the result showed that the proposed method was acceptable in optimizing the BLDCM system. [31]. Figure 4 shows the ACO in a pseudocode format.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Review of The Meta-Heuristic Algorithms for Fuzzy Modeling in The Classification Problem

Aseri¹

2020

IJATCSE

View full text Add to dashboard Cite

Classification is an important tool in today's world, where data is used to make all kinds of decisions in the government, economic, and medicine sectors. Classification is a tool that helps to make sense of the data and find patterns. The increasing size of data has led to more automated approaches as data sets have grown in size and complexity. Fuzzy system is seen as a good approach that can deal with this issue. The fuzzy system works based on implementing fuzzy logic and approximate reasoning. Fuzzy system works well on non-complex issues, but when applied to complex issues such as engineering or medical problems, the construction of the fuzzy system becomes complicated. Due to this, a method is needed where an automatic process to identify the fuzzy parameters is accomplished. There are various algorithms that can be applied to automate the fuzzy modeling. The goal of this study is to review the algorithms that can be applied to automate fuzzy modeling. Based on the literature, Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Simulated Annealing, Gravitational Search Algorithm, Imperialist Competitive Algorithm and Teaching-Learning Based Optimization Algorithm were widely used and applied in fuzzy modeling. A comparative analysis between these algorithms was done to find the strengths and drawbacks of whether these algorithms are suitable to apply in this study. This study focused on the algorithm that is suitable to apply in automated fuzzy modeling and the chosen algorithm will be implemented in a future work.

show abstract

Analysis and comparison among Ant System; Ant Colony System and Max-Min Ant System with different parameters setting

Cited by 25 publications

References 8 publications

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences

Robot Path Planning of Improved Adaptive Ant Colony System Algorithm Based on Dijkstra

Review of The Meta-Heuristic Algorithms for Fuzzy Modeling in The Classification Problem

Contact Info

Product

Resources

About