scholarly journals Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Avinash Sharma ◽  
Rajesh Kumar ◽  
Akash Saxena ◽  
B. K. Panigrahi
Keyword(s):  
Social Structure ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Algorithm Performance ◽  
Novel Approach ◽  
Ensemble Optimization ◽  
Metaheuristic Optimization Algorithm

In this paper, a novel swarm intelligence-based ensemble metaheuristic optimization algorithm, called Structured Clanning-based Ensemble Optimization, is proposed for solving complex numerical optimization problems. The proposed algorithm is inspired by the complex and diversified behaviour present within the fission-fusion-based social structure of the elephant society. The population of elephants can consist of various groups with relationship between individuals ranging from mother-child bond, bond groups, independent males, and strangers. The algorithm tries to model this individualistic behaviour to formulate an ensemble-based optimization algorithm. To test the efficiency and utility of the proposed algorithm, various benchmark functions of different geometric properties are used. The algorithm performance on these test benchmarks is compared to various state-of-the-art optimization algorithms. Experiments clearly showcase the success of the proposed algorithm in optimizing the benchmark functions to better values.

ARTIFICIAL BEE COLONY ALGORITHM WITH IMPROVED EXPLORATIONS: A NOVEL APPROACH FOR NUMERICAL OPTIMIZATION

2014 ◽  
Vol 13 (02) ◽  
pp. 1450010
Author(s):  
MD. SHAFIUL ALAM ◽  
MD. MONIRUL ISLAM ◽  
KAZUYUKI MURASE
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Local Optima ◽  
Selection Scheme ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Approach

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.

scholarly journals AN INTRODUCTION OF KRILL HERD ALGORITHM FOR ENGINEERING OPTIMIZATION

2015 ◽  
Vol 22 (3) ◽  
pp. 302-310 ◽  
Author(s):  
Amir H. GANDOMI ◽  
Amir H. ALAVI
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
State Of The Art ◽  
Engineering Optimization ◽  
Design Problems ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Metaheuristic Optimization Algorithm ◽  
Engineering Optimization Problems ◽  
Better Than

A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is com­pared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

From Optimization to Clustering

2016 ◽  
pp. 1519-1544 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

scholarly journals Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

2020 ◽  
Vol 3 (1) ◽  
pp. 135-188 ◽  
Author(s):  
M. Janga Reddy ◽  
D. Nagesh Kumar
Keyword(s):  
Water Resources ◽  
Evolutionary Algorithms ◽  
Swarm Intelligence ◽  
Water Resource ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Real Life ◽  
Distribution Networks ◽  
Water Resources Engineering

Abstract During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This paper provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.

scholarly journals AntNet: Distributed Stigmergetic Control for Communications Networks

1998 ◽  
Vol 9 ◽  
pp. 317-365 ◽  
Author(s):  
G. Di Caro ◽  
M. Dorigo
Keyword(s):  
Adaptive Learning ◽  
Mobile Agents ◽  
Optimization Problems ◽  
State Of The Art ◽  
Routing Algorithms ◽  
Superior Performance ◽  
Experimental Conditions ◽  
Communications Networks ◽  
Novel Approach ◽  
Routing Tables

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

scholarly journals A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 88026-88041 ◽  
Author(s):  
Yuqi Fan ◽  
Junpeng Shao ◽  
Guitao Sun ◽  
Xuan Shao
Keyword(s):  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Problems
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