A Survey of League Championship Algorithm: Prospects and Challenges <s> LCA in Major Academic Databases <s> Inspired from the competition of sport teams in a sport league, an algorithm is presented for solving constrained optimization problems. A number of individuals (solutions) as sport teams compete in an artificial league for several weeks (iterations). Based on the league schedule in each week, teams play in pairs and their game outcome is determined in terms of win or loss, given known the playing strength (fitness value) along with the teams' intended formations. Modeling an artificial match analysis, each team devises a new formation/ playing strategy (a new solution) for the next week contest and this process is repeated for a number of seasons (stopping condition). Performance of the proposed algorithm is measured using test functions from a well-known benchmark commonly adopted to validate new constraint-handling techniques/ algorithms. Results obtained by the proposed approach are very competitive with respect to other comparator algorithms already developed for constrained optimization and testify that the new algorithm can be regarded as an efficient tool for optimization in the presence of constraints. <s> BIB001 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> LCA in Major Academic Databases <s> In this paper, we develop a new evolutionary algorithm called League Championship Algorithm (LCA) to solve a new single machine scheduling nonlinear problem in JIT environment with batch delivery cost and distinct due dates. Despite its hardness, solving a non-convex function which minimizes earliness and tardiness costs simultaneously seems to be very useful and practical in industrial environments. In order to solve this complex problem, first we present a mathematical model and then design a new discrete nonlinear version of the LCA algorithm which is very efficient and helpful in combinatorial problems, either in terms of computational time or solution quality. <s> BIB002 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> LCA in Major Academic Databases <s> The league championship algorithm (LCA) is a new algorithm originally proposed for unconstrained optimization which tries to metaphorically model a League championship environment wherein artificial teams play in an artificial league for several weeks (iterations). Given the league schedule, a number of individuals, as sport teams, play in pairs and their game outcome is determined given known the playing strength (fitness value) along with the team formation (solution). Modelling an artificial match analysis, each team devises the required changes in its formation (a new solution) for the next week contest and the championship goes for a number of seasons. In this paper, we adapt LCA for constrained optimization. In particular: (1) a feasibility criterion to bias the search toward feasible regions is included besides the objective value criterion; (2) generation of multiple offspring is allowed to increase the probability of an individual to generate a better solution; (3) a diversity mechanism is adopted, which allows infeasible solutions with a promising objective value precede the feasible solutions. Performance of LCA is compared with comparator algorithms on benchmark problems where the experimental results indicate that LCA is a very competitive algorithm. Performance of LCA is also evaluated on well-studied mechanical design problems and results are compared with the results of 21 constrained optimization algorithms. Computational results signify that with a smaller number of evaluations, LCA ensures finding the true optimum of these problems. These results encourage that further developments and applications of LCA would be worth investigating in the future studies. <s> BIB003 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> LCA in Major Academic Databases <s> Inspired by the competition of sport teams in a sport league, the League Championship Algorithm (LCA) has been introduced recently for optimizing nonlinear continuous functions. LCA tries to metaphorically model a league championship environment wherein a number of individuals, as artificial sport teams, play in pairs in an artificial league for several weeks (iterations) based on a league schedule. Given the playing strength (fitness value) along with a team intended formation (solution) in each week, the game outcome is determined in terms of win or loss and this will serve as a basis to direct the search toward fruitful areas. At the heart of LCA is the artificial post-match analysis where, to generate a new solution, the algorithm imitates form the strengths/ weaknesses/ opportunities/ threats (SWOT) based analysis followed typically by coaches to develop a new team formation for their next week contest. In this paper we try to modify the basic algorithm via modeling a between two halves like analysis beside the postmatch SWOT analysis to generate new solutions. Performance of the modified algorithm is tested with that of basic version and the particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmark functions. Results testify that the improved algorithm called RLCA, performs well in terms of both final solution quality and convergence speed. <s> BIB004 </s> A Survey of League Championship Algorithm: Prospects and Challenges <s> LCA in Major Academic Databases <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> BIB005
Four major academic databases were used in this survey (in no particular order) to check the presence of the papers published with the LCA optimization technique. This gives us more inside on how the research community is utilizing this algorithm by its present in the major academic databases for indexing and abstracting. The research databases used for this survey are; the ISI (Thomson Reuters) also called the Web of Science, the Scopus, the IEEE Xplore and the Google Scholar. Table 1 shows a record of research articles that used the LCA in the selected databases as at 31 st January, 2015. It also shows that the LCA have a very high presence in the Google Scholar academic database as at the period under review as almost all the LCA research papers are indexed in it. The Scopus academic database powered by the Elsevier has the second largest indexed documents of the LCA research articles. It contains some articles as at the period under review BIB001 BIB002  BIB005 BIB003 BIB004 , 1) etc. The IEEE Xplore database contains three research documents that implemented the LCA optimization technique BIB001 BIB004 1) . The Web of Science which is the most important scientific database contains about five documents BIB005 BIB003 etc. as at the time under review  . The fact that the LCA only made it to the Web of Science after just about four years shows that the algorithm is not get the desired attention that it deserved.