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Evolutionary algorithms show outstanding performance when they are applied to optimization problems with a few variables, i.e. problems with less than a hundred continuous variables. Large-scale global optimization with continuous variables is still a challenging task for a wide range of evolutionary algorithms. Their performance decreases when the number of variables increases because the search space grows exponentially. Classic evolutionary algorithms cannot find a good solution using the allocated resources. A cooperative coevolution approach is a good tool for increasing the performance of an optimizer in solving high-dimensional problems. The approach splits the objective vector into a predefined number of parts (subcomponents), and each part is optimized by its optimizer. This paper makes an effort to solve the problem of selecting the number of subcomponents. The paper represents a novel metaheuristic for solving optimization problems with a huge number of continuous variables. The suggested approach is based on the self-adaptive cooperation of algorithms and the cooperative coevolution approach. Each algorithm has a unique number of subcomponents. The metaheuristic automatically allocates resources between algorithms during the optimization process. Algorithms optimize the same population one by one. The proposed metaheuristic is titled COSACC, coordination of self-adaptive cooperative coevolution algorithms. We have evaluated the proposed algorithm on fifteen problems from the IEEE LSGO CEC'2013 benchmark. The study demonstrates that COSACC outperforms in average cooperative coevolution algorithms with the static number of subcomponents. Wilcoxon test has proven the results of numerical experiments. We have tested COSACC performance with other state-of-the-art metaheuristics, COSACC is a competitive approach. [ABSTRACT FROM AUTHOR]

A new swarm optimization algorithm known as the sparrow search algorithm (SSA) consider in this work. It fascinated a lot of consideration due to its extremely good attributes. However, it has some pitfalls like minimum global search ability, falling into local optima etc which were found by many researchers. In this paper,first time sparrow search algorithm (SSA) is tested on welded beam design (WBD) optimization problem. Welded beam design (WBD) optimization problem is an engineering optimization problem that has been solved by various metaheuristic algorithms such as SiC-PSO, neuromorphic optimization (NSO) technique etc. Obtained results of SSA were compared with othertechniques such as GWO, PSO, TLBO and it is found that although it works very well on WBD problem with regards to convergence speed, in relation to optimality, convergence accuracy and stability SSA gave the results which were obtained from other algorithms. So, SSA needs to be modified for future work. [ABSTRACT FROM AUTHOR]

In this paper, two new hybrid algorithms are proposed to solve global optimization problems. The first algorithm combines the sooty tern optimization algorithm with the arithmetic optimization algorithm, and the second algorithm combines the sooty tern optimization algorithm with the conjugate gradient method. The sooty tern optimization algorithm is a metaheuristic algorithm proposed by Dhiman and Kaur in 2019 Sooty migration and attack behaviors seabirds in nature were the primary inspiration for this algorithm. In this paper, this algorithm was hybridized using the arithmetic optimization algori thm and the conjugate gradient method, and the two algorithms were measured by applying them to 10 functions, and the results of the hybrid algorithms were very good compared to the original algorithm. [ABSTRACT FROM AUTHOR]

This paper attempts to compare the two socio-inspired based metaheuristics algorithms which were introduced recently. These metaheuristics optimizations and search techniques "Election algorithm, and Election Campaign Optimization Algorithm" were inspired by socio-political processes of human ideologies. This paper gives an overview of these algorithms with respect to their control parameters, components, intensification and diversification as well as their applications. It also makes a simple comparison between the two metaheuristics algorithms in terms of quality of solution, ease of finding correct control parameters and further explored their strengths and limitations as well as their superiority over other metaheuristics algorithms. [ABSTRACT FROM AUTHOR]

In Handwritten Character Recognition (HCR), interest in feature extraction has been on the increase with the abundance of algorithms derived to increase the accuracy of classification. In this paper, a metaheuristic approach for feature extraction technique in HCR based on Crow Search Algorithm (CSA) was proposed. Freeman Chain Code (FCC) was used as data representation. The main problem in representing a character using FCC is that the results of the extractions depend on the starting points that affected the route length of chain code. To solve this problem, the metaheuristic approach via CSA was proposed to find the shortest route length and minimum computational time for HCR. The performance measurements of the proposed CS-FCC extraction algorithm are the route lengths and computation times. The experiments on the algorithms are performed based on the chain code representation derived from established previous works of Center of Excellence for Document Analysis and Recognition (CEDAR) dataset which consists of 126 upper-case letter characters. Based on the result, the proposed CS-FCC obtained 1880.28 in term of route length and only needs 1.10 second to solve the whole character images. [ABSTRACT FROM AUTHOR]

Optimization problems are interest and common problems that are often encountered in life. Optimization can be applied to solve various problems, for example development, government, business, social, economic and something related to the limitation of resource capacity. The most frequently encountered, optimization is often used to find the best solution, that is maximizing profits or minimizing production costs. One of the optimization problems that often occurs is the knapsack problem. There are several types of knapsack problems, one of which is Modified Bounded Knapsack with Multiple Constraints (MBKMC) problem. In popular mathematical studies, metaheuristic algorithms are very often used to solve optimization problems. In this paper the authors did not only use one algorithm, but implemented two metaheuristic algorithms which were combined into one, namely the Gravitational Search Algorithm (GSA) and the Cat Swarm Optimization (CSO) algorithm. The combined algorithm uses the entire GSA algorithm mechanism which is added with the CSO algorithm seeking mode to become the GSA&sCSO algorithm. The author uses the GSA&sCSO algorithm to solve the MBKMC problem of uncertain coefficient. Based on the results of this research, the GSA&sCSO algorithm produces a better solution (higher profit) than the GSA algorithm and the CSO algorithm and earn a better advantage in accordance with the knapsack capacity. In addition, the uncertain coefficient greatly affects the solution obtained, i.e if there is a change of the coefficient, then the solution also changes. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) is a language that bridges real life application to neural network optimization. 2SAT is an interesting paradigm because the outcome of this logical rule is always positive. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when logical rule is embedded to HNN. Increasing the storage capacity during learning phase of HNN has been a challenging problem for most neural network researchers. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. In this paper, metaheuristic algorithm ACO will be proposed in learning 2SAT programming. To this end, all the learning model will be tested in a new restricted learning environment. In circumstances of where HNN model was not given unlimited iteration until all 2SAT clauses were fully satisfied, we would like to determine the better performing search method between exhaustive search and ACO. Results acquired from the computer simulation showed that ACO outperformed exhaustive search in restricted learning environment. [ABSTRACT FROM AUTHOR]

This paper describes the development of dynamic model of flexible beam system using system identification method based on nature inspired algorithm i.e. bat algorithm. At first, input-output data from the experimental rig of flexible beam were collected such that input signal is taken from piezo actuator and output signal from the laser displacement sensor. Then, linear parametric model structure is accomplished using auto regressive with exogenous inputs (ARX). The optimal parameters of the ARX model are identified using bat algorithm. The best parameter setting estimation is selected based on the best fit criterion i.e mean square error (MSE). The identified model is compared with the model from conventional estimation approach. Simulation results show that bat algorithm can outperform the least square algorithm in parametric modelling of the flexible beam. [ABSTRACT FROM AUTHOR]

A general algorithm is presented to approximately solve a great variety of systems of ordinary differential equations (ODEs) independent of their form, order, and given conditions. The systems of ODEs are formulated as optimization problem because it isn't an easy way to get the exact solution for systems of ODEs. Therefore, approximate solution is needed for solving systems of ODEs. One of the approaches used in this paper is using Fourier series expansion to approximate solutions of system of ODEs. The Differential Evolution (DE) algorithm, classified as a metaheuristic algorithm, is used as an optimization method to estimate the most accurate coefficients of Fourier series expansion. In this case, DE will be used to minimize the residual functions of the system of ODEs with Fourier series approximations. The original DE is made by putting the best base vector into the mutation part of the DE algorithm. The results show good performance of DE in solving system of ODEs. [ABSTRACT FROM AUTHOR]

A new fast vector quantization along with a novel fast heuristic optimization algorithm has been proposed in this paper that efficiently boosts the process of finding the global optimum region in the search region. Stochastic variables are used in this algorithm to excel the speed of exploration and exploitation and at the same time making the whole process of vector quantization (VQ) computationally efficient. The search domain gradually shrinks to make algorithm effective and efficient. On comparing the simulation results of VQ based on particle swarm optimization (PSO), quantum particle swarm optimization (QPSO) and firefly algorithm (FFA) using some well- known benchmark functions has validated the efficiency of the algorithm. In finding the global optimum point, the proposed algorithm decreases the computing time 44 % relative to others. Vector quantization based image compression is a NP-hard problem has been successfully and efficiently solved in this proposed work. [ABSTRACT FROM AUTHOR]

This paper considers further development of the method for pre-design studies aimed at improving the reliability, survivability, and safety of complex mechanical systems, assembly units, and parts in accordance with the criteria of strength and resource. The Self-Organization Principle is represented as a combination of a heuristic hierarchical transdisciplinary model and metaheuristic and heuristic rules that automatically create a decision algorithm. The algorithm is determined by the object of study, by the chosen goal, and by the scientific and industrial description of the tasks. The representation of tasks as metaheuristics and heuristics allows for the use of methods and means for artificial intelligence, in particular, expert systems. Metaheuristics and heuristics are compiled by domain experts with further incorporation into the knowledge bases of the corresponding hierarchical complex of Expert Systems and Computational Modules. [ABSTRACT FROM AUTHOR]

Now-a-days mobile robot is used to replace man power due to its ability to mimic activities of human beings. In this context, a trajectory exploration analysis with Khepera-III robot is carried out using modified wind driven optimization (WDO) controller. WDO is a metaheuristic optimization method that draws inspiration from nature. The controller is designed to control the robotic agents during steering in cluttered environments. Here, V-REP software is used to check the navigational capability of proposed controller. With simulation, it is found that the robot successfully achieved the goal post in an optimized way with less time consumption without any collision. [ABSTRACT FROM AUTHOR]

The emergence of mobile robot cannot be overemphasized as it is seen as essential but extreme technology taking into considerations, recent industrialisation. Automation has become a part of the process in order to obtain pathway of collision. The firefly algorithm has been identified to work on the principles of behaviour simulation. Two algorithms are implemented in this regard: Pseudo code of the basic firefly and Pseudo code of the firefly algorithm method that is proposed. To be considered for integration, the firefly algorithm method proposed is taken into consideration. A run of suggested mobile robot environment have been run. In conclusion, metaheuristic algorithms, is inspired by nature and are known as powerful tools especially in tackling global optimisation problem. [ABSTRACT FROM AUTHOR]

In recent years, part of the meta-heuristic optimisation research community has called for a simplification of the algorithmic design: indeed, while most of the state-of-the-art algorithms are characterised by a high level of complexity, complex algorithms are hard to understand and therefore tune for specific real-world applications. Here, we follow this reductionist approach by combining straightforwardly two methods recently proposed in the literature, namely the Re-sampling Inheritance Search (RIS) and the (1+1) Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We propose an RI-(1+1)-CMA-ES algorithm that on the one hand improves upon the original (1+1)-CMA-ES, on the other it keeps the original spirit of simplicity at the basis of RIS. We show with an extensive experimental campaign that the proposed algorithm efficiently solves a large number of benchmark functions and is competitive with several modern optimisation algorithms much more complex in terms of algorithmic design. [ABSTRACT FROM AUTHOR]