Multi-objective volleyball premier league algorithm

This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multiobjective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms

Application of Probabilistic Ranking Systems on Women’s Junior Division Beach Volleyball

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three probabilistic/modern ranking techniques were tested, specifically an Elo variant, TrueSkill, and a random walker graph network. This study found that Elo could predict match outcomes with a 13% higher accuracy than the preexisting systems and TrueSkill with an 11% higher accuracy

The validity, reliability and sensitivity of utilising a wearable GPS based IMU to determine goalkeeper specific training demands

Despite the plethora of football focused literature, there is still very little known about the training practices of the goalkeeper (GK). The development of portable Global Positioning System (GPS) and Inertial Measurement Unit (IMU) devices ensured physical activities can be accurately measured within the training environment. The integration of inertial sensor fusion algorithms has allowed the IMU the ability to also detect non-locomotive activities that are specific to a sport. This technology is shown to be a valid method of analysis for the demands of an outfield football player, however, similar research into the GK position is required. Thus, the aim of this study was to investigate the validity, reliability and sensitivity of utilizing a wearable GPS based IMU to determine goalkeeper specific training demands. A total of 123 event variables were recorded via OptimEye G5 GPS units over 14 sessions from 6 professional GKs during the 2017-2018 Scottish Premiership season. GPS data was collected as part of normal daily monitoring and compared against corresponding computerized notational analysis of the same training sessions. Event variables were split into specific IMU events by a GK specific algorithm: Total Dives (TD), Dives Right (DvR), Dives Left (DvL), Dive Returns (DR) and Jumps. The intra-unit variation was derived from reproducibility of trends within the difference between GPS and corresponding Video Analysis (VA) counts. Unit sensitivity was investigated according to the relationship between average DR times and countermovement jump (CMJ) and ballistic press-up (BP) results which corresponded to lower and upper body velocity at peak power (m/s) respectively. There was no significant difference (p0.05). Bland Altman 95% Limits of Agreement (LOA) show minimal variation for TD (-3.6 to 5.6), DvL (-1.75 to 4.04) and DvR (-3.38 to 3.13). However, DR (-13 to 12.6) and Jumps (-8.8 to 15.7) showed much wider LOA and variation from VA counts. Intra-unit variability was significantly different across all metrics with GPS units, over-estimating movement event counts compared to VA counts. Inter-unit sensitivity suggested that CMJ and lower body velocity at peak power (m/s) performance had the greatest correlation (r=0.992) with average DR times compared to BP and upper body velocity at peak power (r=0.684) and CMJ + BP combined (r=0.603). Based on these findings, the sensitivity of the OptimEye G5 GPS to count GK specific events was almost perfect (r = 903), however, the specificity of the IMU algorithm to distinguish the different movements was questionable. Jumps were significantly over- estimated, and in the meantime, we would suggest using video footage to compliment GPS data for accurate longitudinal analysis. This study provided novel information regarding the DR action, of which the lower body muscular profile plays the dominant part in. Although there are limitations within this study, these investigations should only act as the first step in understanding if the GPS coupled IMU has a place in accurately determining the training demands of a goalkeeper