Soccer League Competition Algorithm Research Articles

FUTBOL LİGİ MÜSABAKASI ALGORİTMASININ DOĞRUSAL OLMAYAN VE BİRBİRİNE BAĞLI GÜÇ SİSTEMLERİNDE YÜK FREKANS KONTROLÜ İÇİN ANALİZİ

Load Frequency Control (LFC) is a significant tool that provides an equilibrium between generating and consuming real power for efficient operation of power system. In this paper, design and performance analysis of Soccer League Competition (SLC) algorithm is provided for optimal solution of LFC in nonlinear interconnected power systems. SLC algorithm has ability of escaping from local minimums while increasing searching capability accuracy with their operators. PID controller parameters are optimized in order to minimize Integral of Time multiplied Absolute Error (ITAE) as objective function. In order to demonstrate efficiency of SLC, two different nonlinear test systems are employed as case studies. At the first instant of study, two area nonlinear thermal power plants with proper value of Generation Rate Constraints (GRC) is considered as test system. Then, the simulations are employed for three-area unequal power system consist of important system limitations such as GRC, Time Delay (TD) and Governor Dead Band (GDB). Simulation results reveal that better dynamic performance of different test systems is obtained using SLC among the considered algorithms.

Using Clustering via Soccer League Competition Algorithm for Optimizing Power Consumption in WSNs (Wireless Sensor Networks)

Using Clustering via Soccer League Competition Algorithm for Optimizing Power Consumption in WSNs (Wireless Sensor Networks)

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.

Designing sustainable concrete mixture by developing a new machine learning technique

Concrete is the most used materials in the construction industry known as an environmental pollutant, posing enormous challenges for sustainability regarding resource depletion, energy consumption, and greenhouse gas emission. Therefore, efforts need to be focused on the reduction of environmental impacts of concrete to boost its sustainability. To develop eco-friendly concrete mixtures, this study aimed to investigate the mixture design of sustainable concrete. To this end, six machine learning techniques, including water cycle algorithm, soccer league competition algorithm, genetic algorithm, artificial neural network, support vector machine, and regression, are applied in order to predict the compressive strength of concrete. The accuracy of these methods is compared based upon performance indicators (e.g., mean absolute error), and the equation generated by the most precision model is utilized for mixture proportioning. Consequently, compressive strength, cost, environmental impacts, including embodied CO2 emission, and energy and resource consumptions are taken into account as sustainability criteria. To integrate these criteria, six types of objective functions are defined and applied and the most efficient sustainable objective function is used to estimate the mixture design of sustainable concrete. Ultimately, the estimated mixtures are compared based on the sustainability index defined by previous studies. The results indicate that water cycle algorithm is the most accurate model with the mean absolute error of 2.86 MPa. Besides, the quadratic distance to the ideal level is the most effective sustainable objective function. Furthermore, increasing the content of cement and super-plasticizer in mixture design deteriorate the sustainability index. Eventually, 16 sustainable mixture proportions are designed, and the most sustainable, the most economical, the eco-friendliest, and the least material-consuming mixtures are presented and compared based on their sustainability indices.

Testing evolutionary algorithms for optimization of water distribution networks

Water distribution networks (WDNs) are one of the most important elements of urban infrastructure and require large investment for construction. Design of WDNs is classified as a large combinatorial discrete nonlinear optimization problem. The main concerns associated with the optimization of such networks are the nonlinearity of the discharge-head loss relationships for pipes and the discrete nature of pipe sizes. Due to these issues, this problem is widely considered to be a benchmark problem for testing and evaluating the performance of nonlinear and heuristic optimization algorithms. This paper compares different techniques, all based on evolutionary algorithms (EAs), which yield optimal solutions for least-cost design of WDNs. All of these algorithms search for the global optimum starting from populations of solutions, rather than from a single solution, as in Newton-based search methods. They use different operators to improve the performance of many solutions over repeated iterations. Ten EAs, four of them for the first time, are applied to the design of three networks and their performance in terms of the least cost, under different stopping criteria, are evaluated. Statistical information for 20 executions of the ten algorithms is summarized, and Friedman tests are conducted. Results show that, for the two-loop benchmark network, the particle swarm optimization gravitational search and biology and bioinformatics global optimization algorithms efficiently converge to the global optimum, but perform poorly for large networks. In contrast, given a sufficient number of function evaluations, the covariance matrix adaptation evolution strategy and soccer league competition algorithm consistently converge to the global optimum, for large networks.

Sports inspired computational intelligence algorithms for global optimization

Many classical search and optimization algorithms are especially insufficient in solving very hard large scale nonlinear problems with stringent constraints. Hence, computational intelligence optimization algorithms have been proposed and used to find well-enough solutions at a reasonable computation time when the classical algorithms are not applicable or do not provide good solutions to these problems due to the unmanageable search space. Many existing algorithms are nature-inspired, which work by simulating or modeling different natural processes. Due to the philosophy of continually searching the best and absence of the most efficient method for all types of problems, novel algorithms or new variants of current algorithms are being proposed and seem to be proposed in future to see if they can cope with challenging optimization problems. Studies on sports in recent years have shown that processes, concepts, rules, and events in various sports can be considered and modelled as novel efficient search and optimization methods with effective exploration capabilities in many cases, which are able to outperform existing classical and computational intelligence based optimization methods within different types of search spaces (Kashan in Appl Soft Comput 16:171–200, 2014; Bouchekara in Oper Res 1–57, 2017; Razmjooy in J Control Autom Electr Syst 1–22, 2016; Osaba et al. in Appl Intell 41(1):145–166, 2014a, Sci World J, 2014b). These novel and interesting sports based algorithms have shown to be more effective and robust than alternative approaches in a large number of applications. In this work, all of the computational intelligence algorithms based on sports and their applications have been for the first time searched and collected. Specific modelling of real sport games for computational intelligence algorithms and their novelties in terms of comparison with alternative existing algorithms for optimization have been reviewed with specific characteristics, computational implementation details and main applications capabilities, in the frame of hard optimization problems. Information is given about these search and optimization algorithms such as League Championship Algorithm, Soccer League Optimization, Soccer Game Optimization, Soccer League Competition Algorithm, Golden Ball Algorithm, World Cup Optimization, Football Optimization Algorithm, Football Game Inspired Algorithm, and Most Valuable Player Algorithm. Performance comparison of these sports based algorithms and other popular algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution within unconstrained global optimization benchmark problems with different characteristics has been performed for the first time. A general evaluation has also been discussed with further research directions.

Testing Soccer League Competition Algorithm in Comparison with Ten Popular Meta-heuristic Algorithms for Sizing Optimization of Truss Structures

Recently, many meta-heuristic algorithms are proposed for optimization of various problems. Some of them originally are presented for continuous optimization problems and some others are just applicable for discrete ones. In the literature, sizing optimization of truss structures is one of the discrete optimization problems which is solved by many meta-heuristic algorithms. In this paper, in order to discover an efficient and reliable algorithm for optimization of truss structures, a discrete optimizer, entitled Soccer League Competition (SLC) algorithm and ten popular and powerful solvers are examined and statistical analysis is carried out for them. The fundamental idea of SLC algorithm is inspired from a professional soccer league and based on the competitions among teams to achieve better ranking and players to be the best. For optimization purpose and convergence of the initial population to the global optimum, different teams compete to take the possession of the best rating positions in the league table and the internal competitions are taken place between players in each team for personal improvements. Recently, SLC as a multi-population algorithm with developed operators has been applied for optimization of various problems. In this paper, for demonstrating the performance of the different solvers for optimal design of truss structures, five numerical examples will be optimized and the results show that proposed SLC algorithm is able to find better solutions among other algorithms. In other words, SLC can discover new local optimal solutions for some examples where other algorithms fail to find that one.

Parameter Improvement of the Soccer League Competition Algorithm by Introducing Stubborn Players: Application to Water Distribution Network

A modified soccer league algorithm is presented in this paper. The effect of stubborn fixed players is investigated and the algorithm is implemented to three benchmark water distribution networks. The modified algorithm is compared to several algorithms. The results show that the modified algorithm performs better than the soccer league competition algorithm, in particular, on the average number of evaluations required to find the optimal cost. Computational results show that the utility benefit of both the individual player and team is essential. The algorithm becomes more reliable when utility benefits are high and as the number of fixed players increases.

Soccer league competition algorithm for solving knapsack problems

Soccer league competition (SLC) algorithm, is a new meta-heuristic optimization technique and has been successfully used to tackle the optimization problems in discrete or continuous space. Fundamental ideas of SLC are inspired by a professional soccer leagues and based on the competitions among teams and players. Population individuals or players are in two types: fixed players and substitutes that all together form some teams. The competition among teams to take the possession of the top ranked positions in the league table and the internal competitions between players in each team for personal improvements are used for simulation purpose and convergence of the population individuals to the global optimum. In this study, an enhanced SLC algorithm is proposed to solve knapsack problems effectively. This new version is free and independent from adjusting the parameters. The experimental results on the some benchmark knapsack problems demonstrate that the proposed SLC is efficient and effective, which outperforms the other algorithms, in terms of the search accuracy, reliability and convergence speed.

Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks

Water distribution networks are one of the most important elements in the urban infrastructure system and require huge investment for construction. Optimal design of water systems is classified as a large combinatorial discrete non-linear optimization problem. The main concern associated with optimization of water distribution networks is related to the nonlinearity of discharge-head loss equation, availability of the discrete nature of pipe sizes, and constraints, such as conservation of mass and energy equations. This paper introduces an efficient technique, entitled Soccer League Competition (SLC) algorithm, which yields optimal solutions for design of water distribution networks. Fundamental theories of the method are inspired from soccer leagues and based on the competitions among teams and players. Like other meta-heuristic methods, the proposed technique starts with an initial population. Population individuals (players) are in two types: fixed players and substitutes that all together form some teams. The competition among teams to take the possession of the top ranked positions in the league table and the internal competitions between players in each team for personal improvements are used for simulation purpose and convergence of the population individuals to the global optimum. Results of applying the proposed algorithm in three benchmark pipe networks show that SLC converges to the global optimum more reliably and rapidly in comparison with other meta-heuristic methods.

Soccer League Competition Algorithm, a New Method for Solving Systems of Nonlinear Equations

This paper introduces Soccer League Competition (SLC) algorithm as a new optimization technique for solving nonlinear systems of equations. Fundamental ideas of the method are inspired from soccer leagues and based on the competitions among teams and players. Like other meta-heuristic methods, the proposed technique starts with an initial population. Population individuals called players are in two types: fixed players and substitutes that all together form some teams. The competition among teams to take the possession of the top ranked positions in the league table and the internal competitions between players in each team for personal improvements results in the convergence of population individuals to the global optimum. Results of applying the proposed algorithm in solving nonlinear systems of equations demonstrate that SLC converges to the answer more accurately and rapidly in comparison with other Meta-heuristic and Newton-type methods.

Soccer League Competition Algorithm, a New Method for Solving Systems of Nonlinear Equations

This paper introduces Soccer League Competition (SLC) algorithm as a new optimization technique for solving nonlinear systems of equations. Fundamental ideas of the method are inspired from soccer leagues and based on the competitions among teams and players. Like other meta-heuristic methods, the proposed technique starts with an initial population. Population individuals called players are in two types: fixed players and substitutes that all together form some teams. The competition among teams to take the possession of the top ranked positions in the league table and the internal competitions between players in each team for personal improvements results in the convergence of population individuals to the global optimum. Results of applying the proposed algorithm in solving nonlinear systems of equations demonstrate that SLC converges to the answer more accurately and rapidly in comparison with other Meta-heuristic and Newton-type methods.