scholarly journals Improved Dragonfly Algorithm with Neighbourhood Structures

2021 ◽  
pp. 303-309
Author(s):  
S. Rajalakshmi ◽  
S. Kanmani ◽  
S. Saraswathi
Keyword(s):  
Search Space ◽  
Engineering Problem ◽  
Gear Train ◽  
Comparative Performance ◽  
Local Optima ◽  
Dragonfly Algorithm ◽  
Best Value ◽  
Neighborhood Structures ◽  
Global Optimal ◽  
Swarming Behaviour

Dragonfly algorithm is a recently proposed optimization algorithm inspired on the static and dynamic swarming behaviour of dragonflies. Because of its simplicity and effectiveness, DA has received interest of specialists from various fields. Premature convergence and local optima is an issue in Dragonfly Algorithm. Improved Dragonfly Algorithm with Neighbourhood Structures (IDANS) is proposed to overcome this drawback. Dragonfly Algorithm with Neighborhood structures utilizes candidate solutions in an iterative and intuitive process to discover promising areas in a search space. IDANS is then initialized with best value of dragonfly algorithm to further explore the search space. In order to improve the efficiency of IDANS, Neighbourhood structures such as Euclidean, Manhattan and Chebyshev are chosen to implement these structures on IDANS to obtain best results. The proposed method avoids local optima to achieve global optimal solutions. The Efficiency of the IDANS is validated by testing on benchmark functions and classical engineering problem called Gear train design problem. A comparative performance analysis between IDANS and other powerful optimization algorithms have been carried out and the results shows that IDANS gives better performance than Dragonfly algorithm. Moreover it gives competitive results in terms of convergence and accuracy when compared with other algorithms in the literature.

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

The Nesting of Two-Dimensional Shapes Using Genetic Algorithms

1995 ◽  
Vol 209 (2) ◽  
pp. 115-124 ◽  
Author(s):  
H S Ismail ◽  
K K B Hon
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Spatial Constraints ◽  
Global Optimal ◽  
Optimum Solution ◽  
Rapid Conversion

The general two-dimensional cutting stock problem is concerned with the optimum layout and arrangement of two-dimensional shapes within the spatial constraints imposed by the cutting stock. The main objective is to maximize the utilization of the cutting stock material. This paper presents some of the results obtained from applying a combination of genetic algorithms and heuristic approaches to the nesting of dissimilar shapes. Genetic algorithms are stochastically based optimization approaches which mimic nature's evolutionary process in finding global optimal solutions in a large search space. The paper discusses the method by which the problem is defined and represented for analysis and introduces a number of new problem-specific genetic algorithm operators that aid in the rapid conversion to an optimum solution.

A Guided Mutation Operator for Dynamic Diversity Enhancement in Evolutionary Strategies

2014 ◽  
Vol 4 (2) ◽  
pp. 20-39
Author(s):  
José L. Guerrero ◽  
Antonio Berlanga ◽  
José M. Molina
Keyword(s):  
Search Space ◽  
Critical Issue ◽  
Population Diversity ◽  
Evolutionary Strategies ◽  
Mutation Operator ◽  
Local Optima ◽  
Guided Mutation ◽  
Statistical Relevance ◽  
Number Of Cycles ◽  
Diversity Enhancement

Diversity in evolutionary algorithms is a critical issue related to the performance obtained during the search process and strongly linked to convergence issues. The lack of the required diversity has been traditionally linked to problematic situations such as early stopping in the presence of local optima (usually faced when the number of individuals in the population is insufficient to deal with the search space). Current proposal introduces a guided mutation operator to cope with these diversity issues, introducing tracking mechanisms of the search space in order to feed the required information to this mutation operator. The objective of the proposed mutation operator is to guarantee a certain degree of coverage over the search space before the algorithm is stopped, attempting to prevent early convergence, which may be introduced by the lack of population diversity. A dynamic mechanism is included in order to determine, in execution time, the degree of application of the technique, adapting the number of cycles when the technique is applied. The results have been tested over a dataset of ten standard single objective functions with different characteristics regarding dimensionality, presence of multiple local optima, search space range and three different dimensionality values, 30D, 300D and 1000D. Thirty different runs have been performed in order to cover the effect of the introduced operator and the statistical relevance of the measured results

scholarly journals Order Reduction based on Coulomb’s and Franklin’s laws Algorithm

2021 ◽  
Author(s):  
Ram Kumar ◽  
Afzal Sikander
Keyword(s):  
Large Scale ◽  
Linear Time ◽  
Order Reduction ◽  
Absolute Error ◽  
Search Space ◽  
Engineering Problem ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Linear Time Invariant

Abstract The Coulomb and Franklin laws (CFL) algorithm is used to construct a lower order model of higher-order continuous time linear time-invariant (LTI) systems in this study. CFL is quite easy to implement in obtaining reduced order model of large scale system in control engineering problem as it employs the combined effect of Coulomb’s and Franklin’s laws to find the best values in search space. The unknown coefficients are obtained using the CFLA methodology, which minimises the integral square error (ISE) between the original and proposed ROMs. To achieve the reduced order model, five practical systems of different orders are considered. Finally, multiple performance indicators such as the ISE, integral of absolute error (IAE), and integral of time multiplied by absolute error were calculated to determine the efficacy of the proposed methodology. The simulation results were compared to previously published well-known research.

Data Clustering Using Sine Cosine Algorithm

2017 ◽  
pp. 715-726 ◽  
Author(s):  
Vijay Kumar ◽  
Dinesh Kumar
Keyword(s):  
Real Life ◽  
Quality Measures ◽  
Search Space ◽  
Search Method ◽  
Local Optima ◽  
Encoding Scheme ◽  
Clustering Techniques ◽  
Sine Cosine Algorithm ◽  
Cluster Quality ◽  
Optimal Cluster

The clustering techniques suffer from cluster centers initialization and local optima problems. In this chapter, the new metaheuristic algorithm, Sine Cosine Algorithm (SCA), is used as a search method to solve these problems. The SCA explores the search space of given dataset to find out the near-optimal cluster centers. The center based encoding scheme is used to evolve the cluster centers. The proposed SCA-based clustering technique is evaluated on four real-life datasets. The performance of SCA-based clustering is compared with recently developed clustering techniques. The experimental results reveal that SCA-based clustering gives better values in terms of cluster quality measures.

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

2022 ◽  
pp. 166-201
Author(s):  
Asha Gowda Karegowda ◽  
Devika G.
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Parameter Optimization ◽  
Heuristic Algorithms ◽  
Search Space ◽  
Optimization Method ◽  
Classification Problems ◽  
Local Optima ◽  
Consistent Solution ◽  
Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

scholarly journals A Variable Neighborhood MOEA/D for Multiobjective Test Task Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Hui Lu ◽  
Zheng Zhu ◽  
Xiaoteng Wang ◽  
Lijuan Yin
Keyword(s):  
Task Scheduling ◽  
Search Space ◽  
Global Optimum ◽  
Scheduling Problem ◽  
Local Optima ◽  
Test Task ◽  
Combinational Optimization ◽  
The Mean ◽  
Nondominated Sorting ◽  
Better Than

Test task scheduling problem (TTSP) is a typical combinational optimization scheduling problem. This paper proposes a variable neighborhood MOEA/D (VNM) to solve the multiobjective TTSP. Two minimization objectives, the maximal completion time (makespan) and the mean workload, are considered together. In order to make solutions obtained more close to the real Pareto Front, variable neighborhood strategy is adopted. Variable neighborhood approach is proposed to render the crossover span reasonable. Additionally, because the search space of the TTSP is so large that many duplicate solutions and local optima will exist, the Starting Mutation is applied to prevent solutions from becoming trapped in local optima. It is proved that the solutions got by VNM can converge to the global optimum by using Markov Chain and Transition Matrix, respectively. The experiments of comparisons of VNM, MOEA/D, and CNSGA (chaotic nondominated sorting genetic algorithm) indicate that VNM performs better than the MOEA/D and the CNSGA in solving the TTSP. The results demonstrate that proposed algorithm VNM is an efficient approach to solve the multiobjective TTSP.

Local Optima Avoidance in GA Biclustering using Map Reduce

2016 ◽  
Vol 6 (1) ◽  
pp. 37-47
Author(s):  
Gowri R. ◽  
Rathipriya R.
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Population Size ◽  
Optimal Solution ◽  
Search Space ◽  
Gene Expression Dataset ◽  
Map Reduce ◽  
Mapreduce Framework ◽  
Local Optima ◽  
Novel Approach

One of the prominent issues in Genetic Algorithm (GA) is premature convergence on local optima. This restricts the enhanced optimal solution searching in the entire search space. Population size is one of the influencing factors in Genetic Algorithm. Increasing the population size will improvise the randomized searching and maintains the diversity in the population. It also increases its computational complexity. Especially in GA Biclustering (GABiC), the search should be randomized to find more optimal patterns. In this paper, a novel approach for population setup in MapReduce framework is proposed. The maximal population is split into population sets, and these groups will proceed searching in parallel using MapReduce framework. This approach is attempted for biclustering the gene expression dataset in this paper. The performance of this proposed work seems promising on comparing its results with those obtained from previous hybridized optimization approaches. This approach will also handle data scalability issues and applicable to the big data biclustering problems.

An Intelligent Operator for Genetic Fuzzy Rule Based System

2011 ◽  
Vol 7 (3) ◽  
pp. 28-40 ◽  
Author(s):  
C. Rani ◽  
S. N. Deepa
Keyword(s):  
Fuzzy Rule ◽  
Bench Mark ◽  
Genetic Operators ◽  
Local Optima ◽  
Rule Based ◽  
Rule Based System ◽  
Usual Procedure ◽  
Best Value ◽  
Minimum Number ◽  
Information Sharing Mechanism

This paper proposes a modified form of operator based on Particle Swarm Optimization (PSO) for designing Genetic Fuzzy Rule Based System (GFRBS). The usual procedure of velocity updating in PSO is modified by calculating the velocity using chromosome’s individual best value and global best value based on an updating probability without considering the inertia weight, old velocity and constriction factors. This kind of calculation brings intelligent information sharing mechanism and memory capability to Genetic Algorithm (GA) and can be easily implemented along with other genetic operators. The performance of the proposed operator is evaluated using ten publicly available bench mark data sets. Simulation results show that the proposed operator introduces new material into the population, thereby allows faster and more accurate convergence without struck into a local optima. Statistical analysis of the experimental results shows that the proposed operator produces a classifier model with minimum number of rules and higher classification accuracy.

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