Exact Optimization Algorithms for Complex Problems

2007 ◽  
pp. 15-29
Keyword(s):  
Optimization Algorithms ◽  
Complex Problems ◽  
Exact Optimization

Analytical Review on the Modern Optimization Algorithms in Logistics

2020 ◽  
Vol 14 (1) ◽  
pp. 25-31
Author(s):  
Mohammad Zaher Akkad ◽  
Tamás Bányai
Keyword(s):  
Content Analysis ◽  
Optimization Algorithms ◽  
Hybrid Algorithms ◽  
Metaheuristic Algorithms ◽  
Complex Problems ◽  
Analytical Review ◽  
Optimum Solution ◽  
Short Time ◽  
Modern Optimization

Optimization algorithms are used to reach the optimum solution from a set of available alternatives within a short time relatively. With having complex problems in the logistics area, the optimization algorithms evolved from traditional mathematical approaches to modern ones that use heuristic and metaheuristic approaches. Within this paper, the authors present an analytical review that includes illustrative and content analysis for the used modern algorithms in the logistics area. The analysis shows accelerated progress in using the heuristic/metaheuristic algorithms for logistics applications. It also shows the strong presence of hybrid algorithms that use heuristic and metaheuristic approaches. Those hybrid algorithms are providing very efficient results.

scholarly journals A Survey on Meta-heuristic Algorithms for Global Optimization Problems

2020 ◽  
pp. 48-60
Author(s):  
Abdel Nasser H. Zaied ◽  
Mahmoud Ismail ◽  
Salwa El-Sayed ◽  
◽  
◽  
...  
Keyword(s):  
Global Optimization ◽  
Real World ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Traditional Methods ◽  
Complex Problems ◽  
First Choice ◽  
Important Field ◽  
Real World Problems

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

scholarly journals Mexican Axolotl Optimization: A Novel Bioinspired Heuristic

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 781
Author(s):  
Yenny Villuendas-Rey ◽  
José L. Velázquez-Rodríguez ◽  
Mariana Dayanara Alanis-Tamez ◽  
Marco-Antonio Moreno-Ibarra ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Computational Complexity ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Cuckoo Search ◽  
Search Space ◽  
Monarch Butterfly ◽  
Mexican Axolotl ◽  
Dragonfly Algorithm ◽  
Whale Optimization ◽  
Exact Optimization

When facing certain problems in science, engineering or technology, it is not enough to find a solution, but it is essential to seek and find the best possible solution through optimization. In many cases the exact optimization procedures are not applicable due to the great computational complexity of the problems. As an alternative to exact optimization, there are approximate optimization algorithms, whose purpose is to reduce computational complexity by pruning some areas of the problem search space. To achieve this, researchers have been inspired by nature, because animals and plants tend to optimize many of their life processes. The purpose of this research is to design a novel bioinspired algorithm for numeric optimization: the Mexican Axolotl Optimization algorithm. The effectiveness of our proposal was compared against nine optimization algorithms (artificial bee colony, cuckoo search, dragonfly algorithm, differential evolution, firefly algorithm, fitness dependent optimizer, whale optimization algorithm, monarch butterfly optimization, and slime mould algorithm) when applied over four sets of benchmark functions (unimodal, multimodal, composite and competition functions). The statistical analysis shows the ability of Mexican Axolotl Optimization algorithm of obtained very good optimization results in all experiments, except for composite functions, where the Mexican Axolotl Optimization algorithm exhibits an average performance.

Coupling of Optimization Algorithms Based on Swarm Intelligence

2018 ◽  
pp. 27-50 ◽  
Author(s):  
Kamalanand Krishnamurthy ◽  
Mannar Jawahar Ponnuswamy
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Computational Intelligence ◽  
Biomedical Engineering ◽  
Optimization Algorithms ◽  
Bacterial Foraging Optimization ◽  
Complex Problems ◽  
Bacterial Foraging

Swarm intelligence is a branch of computational intelligence where algorithms are developed based on the biological examples of swarming and flocking phenomena of social organisms such as a flock of birds. Such algorithms have been widely utilized for solving computationally complex problems in fields of biomedical engineering and sociology. In this chapter, two different swarm intelligence algorithms, namely the jumping frogs optimization (JFO) and bacterial foraging optimization (BFO), are explained in detail. Further, a synergetic algorithm, namely the coupled bacterial foraging/jumping frogs optimization algorithm (BFJFO), is described and utilized as a tool for control of the heroin epidemic problem.

Performance Analysis of Optimization Process on Adaptive Group of Ink Drop Spread Algorithm Proficiency

2020 ◽  
Vol 13 (6) ◽  
pp. 918-924
Author(s):  
Iman E.P. Afrakoti ◽  
Vahdat Nazerian
Keyword(s):  
System Modeling ◽  
Optimization Algorithms ◽  
Computation Time ◽  
Critical Parameters ◽  
Optimization Approach ◽  
Complex Problems ◽  
Real World Problem ◽  
Active Learning Method ◽  
Processing Resources ◽  
Complex Optimization

Aims: Two evolutionary algorithms consist of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are being used for finding the best value of critical parameters in AGIDS which will affect the accuracy and efficiency of the algorithm. Background: Adaptive Group of Ink Drop Spread (AGIDS) is a powerful algorithm which was proposed in fuzzy domain based on Active Learning Method (ALM) algorithm. Objective: The effectiveness of AGIDS vs. artificial neural network and other soft-computing algorithms has been shown in classification, system modeling and regression problems. Methods: For solving a real-world problem a tradeoff should be taken between the needed accuracy and the available time and processing resources. Results: The simulation result shows that optimization approach will affect the accuracy of modelling being better, but its computation time is rather high. Conclusion: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms. Other: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms.

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