A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> To solve the premature convergence problem of the Particle Swarm Optimization(PSO),an improved PSO method was proposed.In the improved method,the "uphill" movement of SA was introduced and the operations of crossover and mutation was used to keep the diversity of the population.The effects of Gauss mutation operator and the Cauchy mutation operator on the improved algorithm were compared.The optimal results on benchmark functions demonstrate that the proposed method can get the global optimal result more quicky and more efficiently than the basic PSO,and the improved algorithm which use Cauchy mutation operator outperforms the algorithm which use Guass mutation operator regarding the quality of solutions on benchmark functions. <s> BIB001 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> Clustering analysis is applied generally to Pattern Recognition, Color Quantization and Image Classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model[1, 2, 3J. This method is quite simple and valid and it can avoid the minimum local value. Finally, the effectiveness of the PSO-clustering is demonstrated on four artificial data sets. <s> BIB002 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> Abstract In this paper, a novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors: (i) movement induced by the presence of other individuals (ii) foraging activity, and (iii) random diffusion. For more precise modeling of the krill behavior, two adaptive genetic operators are added to the algorithm. The proposed method is verified using several benchmark problems commonly used in the area of optimization. Further, the KH algorithm is compared with eight well-known methods in the literature. The KH algorithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms. <s> BIB003 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> Over the last decade, we have encountered various complex optimization problems in the engineering and research domains. Some of them are so hard that we had to turn to heuristic algorithms to obtain approximate optimal solutions. In this paper, we present a novel metaheuristic algorithm called mussels wandering optimization (MWO). MWO is inspired by mussels’ leisurely locomotion behavior when they form bed patterns in their habitat. It is an ecologically inspired optimization algorithm that mathematically formulates a landscape-level evolutionary mechanism of the distribution pattern of mussels through a stochastic decision and Levy walk. We obtain the optimal shape parameter μ of the movement strategy and demonstrate its convergence performance via eight benchmark functions. The MWO algorithm has competitive performance compared with four existing metaheuristics, providing a new approach for solving complex optimization problems. <s> BIB004 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> Cat Swarm Optimization (CSO) is an evolutionary method inspired by the creatures in Mother Nature for solving optimization problems. Less than one decade after CSO is proposed, it has been improved and applied in different fields by many researchers in recent years. In this review, we majorly focus on the original CSO algorithm and some improved branches of CSO family algorithms. Some examples of utilizing CSO to solve problems in engineering are also reviewed. In the first section, the concept of the first proposed CSO algorithm is given in brief, and the improved Parallel CSO (PCSO) is described and summarized in a comprehensive way. Finally, the optimization problems in the engineering fields solved by CSO family algorithms are also reviewed. The objective of the review is to provide the overview of CSO algorithms and a brief introduction for new researchers to the swarm intelligence research field. <s> BIB005 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> Abstract Optimal capacitor placement as a precious and significant strategy to improve the performance of the power systems has attracted much attention in recent years. However, the non-linear and discrete nature of the optimal capacitor placement problem is a big barrier in front of the traditional optimization methods for global search. Therefore, this article proposes a powerful optimization technique based adaptive modified honey bee mating optimization algorithm to solve the optimal capacitor placement problem effectively. The proposed method consists of two sub-modification methods to let each solution choose the modification methods which best fit its situation. Also, in order to preserve the random search ability of the algorithm, the idea of the roulette wheel mechanism is employed in the selection process. The proposed algorithm can overcome the main shortcomings in the traditional honey bee mating optimization algorithm by using both local and global search schemes. Therefore, the problem inve... <s> BIB006 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> Natural phenomenon can be used to solve complex optimization problems with its excellent facts, functions, and phenomenon. In this paper, a survey on physics-based algorithm is done to show how these inspirations led to the solution of well-known optimization problem. The survey is focused on inspirations that are originated from physics, their formulation into solutions, and their evolution with time. Comparative studies of these noble algorithms along with their variety of applications have been done throughout this paper. <s> BIB007 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> Introduction <s> League Championship Algorithm (LCA) is a recently proposed stochastic population based algorithm for continuous global optimization which tries to mimic a championship environment wherein artificial teams play in an artificial league for several weeks (iterations). Given the league schedule in each week, a number of individuals as sport teams play in pairs and their game outcome is determined in terms of win or loss (or tie), given the playing strength (fitness value) along with the intended team formation/arrangement (solution) developed by each team. Modeling an artificial match analysis, each team devises the required changes in its formation (generation of a new solution) for the next week contest and the championship goes on for a number of seasons (stopping condition). An add-on module based on modeling the end season transfer of players is also developed to possibly speed up the global convergence of the algorithm. Extensive analysis to verify the rationale of the algorithm and suitability of the updating equations together with investigating the effect of different settings for the control parameters are carried out empirically on a large number of benchmark functions. Results indicate that LCA exhibits promising performance suggesting that its further developments and practical applications would be worth investigating in the future studies. <s> BIB008
League Championship Algorithm (LCA) is a population based algorithmic framework for global optimization over a continuous search space first proposed by Kashan 1 . It is a Swarm optimization algorithm [2] [3] BIB001 BIB002 . A general characteristic between all population based optimization algorithms similar to the LCA is that they both try to improve a population of achievable solutions to potential areas of the search space when seeking the optimization. LCA is a newly proposed stochastic population based algorithm for continuous global optimization which tries to imitate a championship situation where synthetic football clubs participate in an artificial league for a number of weeks. This algorithm has been tested in many areas and performed creditably well as compared to other known optimization schemes or heuristics algorithms BIB008 . Optimization algorithms are dynamic research areas with rapidly increasing literatures. It is a common knowledge among researchers that combinatorial optimization problems like the travelling salesman problem are non-deterministic polynomial times (NP-hard) problems   , which implies that there are no fast solutions or exact solutions for such problems. Some of the most effective solutions to NP-hard problems are to utilize heuristic or metaheuristic algorithms. Heuristic algorithms are to look for solutions using trial and error, while the metaheuristic algorithm can be seen as a higher-level algorithm that use information and selection of the solutions to guide optimization problems with the intention of finding the optimality, even though such optimality is not mostly attainable. But many discrete and continues optimization problems are NP-complete, optimal solution techniques which we can execute as a polynomial time is also not attainable with a very high probability. Therefore, these types of problems are tackled by the construction of heuristics, metaheuristics or approximation algorithms which run in polynomial time and are often the best known method to find efficient solutions  BIB005 . In recent times, many inspirational optimization algo rithms have gained large popularity among researchers in solving artificial intelligence, machine learning, computational intelligence, engineering applications, distributed computing (parallel, grid and cloud) and data mining. These includes; nature-inspired optimization algorithms  , bio-inspired optimization algorithms BIB003 , evolutionary algorithms BIB006 , physics-inspired optimization algorithms BIB007 , ecologically-inspired optimization algorithms BIB004 and now sports-inspired optimization algorithms BIB008 . The LCA is a sport-inspired optimization algorithm which is also a type of swarm algorithm. It has been used in several researches in comparison with other established techniques to evaluate its efficiency and in most cases the results obtained are quite remarkable. The aim of this paper is to survey all available research articles on the League Championship Algorithm (LCA) and its applications. We also wish to highlight some potential application areas that are yet to be exploited and some challenges. Therefore, the paper is organised as follows: Section 2 presents the LCA as it appears in previous literatures. Section 3 reviews in brief, a synopsis of the LCA researches. Section 4 investigates the acceptability of the LCA algorithm within the academic community. Section 5 presents the future prospects of using the LCA and its applications in other research areas. Section 6 describes some of the challenges faced by the users of the LCA algorithms and section 7 is the conclusion of the survey.