scholarly journals Random search with k -prototypes algorithm for clustering mixed datasets

2011 ◽  
Vol 467 (2132) ◽  
pp. 2387-2403 ◽  
Author(s):  
Duc-Truong Pham ◽  
Maria M. Suarez-Alvarez ◽  
Yuriy I. Prostov
Keyword(s):  
Optimization Algorithm ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
The Other ◽  
Bees Algorithm ◽  
Neighbourhood Search ◽  
Promising Solution ◽  
Number Of Iterations ◽  
Selection Of

A new algorithm to cluster datasets with mixed numerical and categorical values is presented. The algorithm, called RANKPRO (random search with k -prototypes algorithm), combines the advantages of a recently introduced population-based optimization algorithm called the bees algorithm (BA) and k -prototypes algorithm. The BA works with elite and good solutions, and continues to look for other possible extrema solutions keeping the number of testing points constant. However, the improvement of promising solutions by the BA may be time-consuming because it is based on random neighbourhood search. On the other hand, an application of the k -prototypes algorithm to a promising solution may be very effective because it improves the solution at each iteration. The RANKPRO algorithm balances two objectives: it explores the search space effectively owing to random selection of new solutions, and improves promising solutions fast owing to employment of the k -prototypes algorithm. The efficiency of the new algorithm is demonstrated by clustering several datasets. It is shown that in the majority of the considered datasets when the average number of iterations that the k -prototypes algorithm needs to converge is over 10, the RANKPRO algorithm is more efficient than the k -prototypes algorithm.

Neural Network Weight Optimisation Using the Bees Algorithm

2010 ◽  
Author(s):  
Afshin Ghanbarzadeh
Keyword(s):  
Foraging Behaviour ◽  
Search Algorithm ◽  
Random Search ◽  
Identification Problem ◽  
Population Based ◽  
Bees Algorithm ◽  
Defect Identification ◽  
Neighbourhood Search ◽  
Network Weight ◽  
Population Based Search

This paper presents an application of the Bees Algorithm (BA) to the optimisation of weights within neural networks for wood defect detection. This novel population-based search algorithm mimics the natural foraging behaviour of swarms of bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search. Following a brief description of the algorithm, the paper gives the results obtained for the wood defect identification problem demonstrating the efficiency and robustness of the new algorithm.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

Evaluation of several initialization methods on arithmetic optimization algorithm performance

2021 ◽  
Vol 31 (1) ◽  
pp. 70-94
Author(s):  
Jeffrey O. Agushaka ◽  
Absalom E. Ezugwu
Keyword(s):  
Population Size ◽  
Optimization Algorithm ◽  
Beta Distribution ◽  
Random Number ◽  
Large Population ◽  
Latin Hypercube Sampling ◽  
Rayleigh Distribution ◽  
Population Based ◽  
Algorithm Performance ◽  
Number Of Iterations

Abstract Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competitive with the Random number. On the basis of our experiments’ results, we recommend that a solution size of 6,000, the number of iterations of 100, and initializing the solutions with Beta distribution will lead to AOA performing optimally for scenarios considered in our experiments.

Swarm Intelligence for Multi-Objective Optimization in Engineering Design

2018 ◽  
pp. 239-250 ◽  
Author(s):  
Janga Reddy Manne
Keyword(s):  
Swarm Intelligence ◽  
Engineering Design ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
Design Problems ◽  
Multi Objective ◽  
Hard Constraints ◽  
Decision Variables ◽  
Engineering Design Problems

Most of the engineering design problems are intrinsically complex and difficult to solve, because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these kind of complexities. The SI algorithms being population based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived based on principles of co-operative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization, and swarm optimization for solving multi-objective engineering design problems with illustration through few examples.

scholarly journals Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhaojuan Zhang ◽  
Wanliang Wang ◽  
Ruofan Xia ◽  
Gaofeng Pan ◽  
Jiandong Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Experimental Results ◽  
The Other ◽  
Ancestral Genome ◽  
Swarm Optimization ◽  
Median Approach

Abstract Background Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. Results In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. Conclusions Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

scholarly journals Fertilization optimization algorithm on CEC2015 and large scale problems

2021 ◽  
Author(s):  
Hazim Nasir Ghafil ◽  
Shaymaa Alsamia ◽  
Károly Jármai
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Random Search ◽  
Search Space ◽  
Large Scale Optimization ◽  
Biologically Inspired ◽  
Large Scale Problems ◽  
Comparison Results ◽  
Scale Optimization

Abstract This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.

Swarm Intelligence for Multi-Objective Optimization in Engineering Design

2019 ◽  
pp. 180-194
Author(s):  
Janga Reddy Manne
Keyword(s):  
Swarm Intelligence ◽  
Engineering Design ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
Design Problems ◽  
Multi Objective ◽  
Hard Constraints ◽  
Decision Variables ◽  
Engineering Design Problems

Most of the engineering design problems are intrinsically complex and difficult to solve because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives, and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these complexities. The SI algorithms, being population-based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived from principles of cooperative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization and swarm optimization for solving multi-objective engineering design problems with illustration through a few examples.

scholarly journals Simulated Annealing with Exploratory Sensing for Global Optimization

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 230
Author(s):  
Majid Almarashi ◽  
Wael Deabes ◽  
Hesham H. Amin ◽  
Abdel-Rahman Hedar
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Initial Temperature ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Algorithms ◽  
Two Phases ◽  
Number Of Iterations

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.

scholarly journals A Comparison of Global Search Algorithms for Continuous Black Box Optimization

2012 ◽  
Vol 20 (4) ◽  
pp. 509-541 ◽  
Author(s):  
Petr Pošík ◽  
Waltraud Huyer ◽  
László Pál
Keyword(s):  
State Of The Art ◽  
Search Space ◽  
Search Algorithms ◽  
Black Box ◽  
Search Method ◽  
The Other ◽  
Systematic Sampling ◽  
Careful Selection ◽  
Local Optimizer ◽  
Selection Of

Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The recently proposed “comparing continuous optimizers” (COCO) methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms (EAs) with features of the other compared algorithms.

scholarly journals An Efficient ACO+RVND for Solving the Resource-Constrained Deliveryman Problem

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ban Ha Bang
Keyword(s):  
Ant Colony Algorithm ◽  
State Of The Art ◽  
Random Variable ◽  
Search Space ◽  
Population Based ◽  
Resource Constrained ◽  
Variable Neighborhood Descent ◽  
Combinatorial Explosion ◽  
Promising Solution ◽  
Population Based Algorithm

In this paper, Resource-Constrained Deliveryman Problem (RCDMP) is introduced. The RCDMP problem deals with finding a tour with minimum waiting time sum so that it consumes not more than the $R_{max}$ unites of the resources, where $R_{max}$ is some constant. Recently, an algorithm developed in a trajectory-based metaheuristic has been proposed. Since the search space of the problem is a combinatorial explosion, the trajectory-based sequential can only explore a subset of the search space, therefore, they easily fall into local optimal in some cases. To overcome the drawback of the current algorithms, we propose a population-based algorithm that combines an Ant Colony Algorithm (ACO), and Random Variable Neighborhood Descent (RVND). In the algorithm, ACO explores the promising solution areas while RVND exploits them with the hope of improving a solution. Extensive numerical experiments and comparisons with the state-of-the-art metaheuristic algorithms in the literature show that the proposed algorithm reaches better solutions in many cases.

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