A New Multiagent Algorithm for Dynamic Continuous Optimization

2010 ◽  
Vol 1 (1) ◽  
pp. 16-38 ◽  
Author(s):  
Julien Lepagnot ◽  
Amir Nakib ◽  
Hamouche Oulhadj ◽  
Patrick Siarry
Keyword(s):  
Dynamic Optimization ◽  
Optimization Algorithm ◽  
Real World ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Dynamic Problems ◽  
Moving Peaks Benchmark ◽  
Moving Peaks ◽  
Over Time ◽  
Real World Problems

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.

A New Multiagent Algorithm for Dynamic Continuous Optimization

2012 ◽  
pp. 131-153
Author(s):  
Julien Lepagnot ◽  
Amir Nakib ◽  
Hamouche Oulhadj ◽  
Patrick Siarry
Keyword(s):  
Dynamic Optimization ◽  
Optimization Algorithm ◽  
Real World ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Dynamic Problems ◽  
Moving Peaks Benchmark ◽  
Moving Peaks ◽  
Over Time ◽  
Real World Problems

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.

scholarly journals A New Optimization Algorithm Based on Search and Rescue Operations

2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Amir Shabani ◽  
Behrouz Asgarian ◽  
Saeed Asil Gharebaghi ◽  
Miguel A. Salido ◽  
Adriana Giret
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Search And Rescue ◽  
Rank Test ◽  
Engineering Design Problems ◽  
Continuous Optimization Problems

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

CMCABC: Clustering and Memory-Based Chaotic Artificial Bee Colony Dynamic Optimization Algorithm

2018 ◽  
Vol 17 (04) ◽  
pp. 1007-1046 ◽  
Author(s):  
Mohsen Moradi ◽  
Samad Nejatian ◽  
Hamid Parvin ◽  
Vahideh Rezaie
Keyword(s):  
Dynamic Optimization ◽  
Real World ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Dynamic Environments ◽  
Dynamic Optimization Problems ◽  
The Real ◽  
Bee Colony ◽  
Swarm Intelligence Optimization

The swarm intelligence optimization algorithms are used widely in static purposes and applications. They solve the static optimization problems successfully. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments as well. The dynamic optimization problems indicate the ones whose solutions change over time. The artificial bee colony algorithm is one of the swarm intelligence optimization algorithms. In this study, a clustering and memory-based chaotic artificial bee colony algorithm, denoted by CMCABC, has been proposed for solving the dynamic optimization problems. A chaotic system has a more accurate prediction for future in the real-world applications compared to a random system, because in the real-world chaotic behaviors have emerged, but random behaviors havenot been observed. In the proposed CMCABC method, explicit memory has been used to save the previous good solutions which are not very old. Maintaining diversity in the dynamic environments is one of the fundamental challenges while solving the dynamic optimization problems. Using clustering technique in the proposed method can well maintain the diversity of the problem environment. The proposed CMCABC method has been tested on the moving peaks benchmark (MPB). The MPB is a good simulator to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMCABC method compared to the other state-of-the-art methods in solving dynamic optimization problems.

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.

A Novel BFO Optimization Algorithm with Neighborhood Learning

2014 ◽  
Vol 543-547 ◽  
pp. 1888-1891
Author(s):  
Li Jing Tan ◽  
Fu Yong Lin ◽  
Ben Niu ◽  
Qi Qi Duan ◽  
Kai Yin
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Real World ◽  
Convergence Speed ◽  
Local Minima ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Simulation Results ◽  
Swarm Intelligence Algorithm ◽  
Real World Problems

Bacterial foraging optimization is a relatively new bio-inspired swarm intelligence algorithm and has been successfully applied to solve many real-world problems. However, similar to other swarm intelligence algorithms, BFO also faces up to some challenging problems, such as low convergence speed and easily to be trapped into local minima. To deal with these issues, we incorporate the concept of neighbor topology and the idea of neighbor learning to improve the performance of BFO, called bacterial foraging optimization with neighborhood learning (BFO-NL). Simulation results demonstrated the good performance of our proposed BFO-NL when compared with original BFO.

scholarly journals Donkey and smuggler optimization algorithm: A collaborative working approach to path finding

2019 ◽  
Vol 6 (4) ◽  
pp. 562-583 ◽  
Author(s):  
Ahmed S. Shamsaldin ◽  
Tarik A. Rashid ◽  
Rawan A. Al-Rashid Agha ◽  
Nawzad K. Al-Salihi ◽  
Mokhtar Mohammadi
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Experimental Results ◽  
Optimization Approach ◽  
Packet Routing ◽  
Real World Data ◽  
Search Spaces ◽  
Searching Ability ◽  
Benchmark Test Functions ◽  
Real World Problems

AbstractSwarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems.Highlights Donkey and Smuggler Optimization Algorithm (DSO) is a novel optimization algorithm. The DSO is inspired by Donkeys' behaviors. It has been evaluated against other algorithms. DSO solves different problems such as TSP, packet routing, and ambulance routing. The outcomes on the tests exhibit a greater manipulation of DSO. The outcomes prove that DSO has a greater searching ability.

Comparative Study of Meta-heuristics Optimization Algorithm using Benchmark Function

2017 ◽  
Vol 7 (3) ◽  
pp. 1643 ◽  
Author(s):  
I. Ismail ◽  
A. Hanif Halim
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Computation Time ◽  
Global Optimum ◽  
Performance Comparison ◽  
Difficulty Level ◽  
Problem Type ◽  
Benchmark Function ◽  
Real World Application ◽  
Real World Problems

<span lang="EN-US">Meta-heuristics optimization is becoming a popular tool for solving numerous problems in real-world application due to the ability to overcome many shortcomings in traditional optimization. Despite of the good performance, there is limitation in some algorithms that deteriorates by certain degree of problem type. Therefore it is necessary to compare the performance of these algorithms with certain problem type. This paper compares 7 meta-heuristics optimization with 11 benchmark functions that exhibits certain difficulties and can be assumed as a simulation relevant to the real-world problems. The tested benchmark function has different type of problem such as modality, </span><span lang="EN-MY">separability</span><span lang="EN-US">, discontinuity and surface effects with steep-drop global optimum, bowl- and plateau-typed function. Some of the proposed function has the combination of these problems, which might increase the difficulty level of search towards global optimum. The performance comparison includes computation time and convergence of global optimum.</span>

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