Scheduling the English Football League with a Multi-objective Evolutionary Algorithm

While, R. Lyndon; Kendall, Graham

doi:10.1007/978-3-319-10762-2_83

Cited by 7 publications

(3 citation statements)

References 12 publications

(31 reference statements)

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“…EAs are population-based techniques and provide good approximated solutions in single simulation unlike the traditional mathematical programming techniques. EAs are population-based metaheuristics [7][8][9] and are generally divided into nine different groups: the biology-based [10,11], physics-based [12,13], social-based [14][15][16][17], music-based [18], chemical-based [19], sport-based [20], mathematics-based [21], swarm-based [22][23][24][25][26][27][28], and hybrid methods [29][30][31][32][33][34][35][36]. Genetic algorithm (GA) [37], evolutionary strategy (ES) [38], evolutionary programming (EP) [39,40], and genetic programming (GP) [41] are the classical paradigms of evolutionary computing [8].…”

Section: Introductionmentioning

confidence: 99%

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

2021

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Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.

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Section: Introductionmentioning

confidence: 99%

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

2021

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“…EAs are nature-inspired-based techniques [8][9][10]. In general, EAs can be categorized into nine different groups including the biology-based [11,12], physics-based [13,14], social-based [15][16][17][18], music-based [19], chemical-based [20], sport-based [21], mathematics-based [22], swarm-based [23][24][25][26][27][28][29], and hybrid methods [30][31][32][33][34][35][36][37]. All these algorithms operate on a uniform and random set of solutions generated within the search domain of the given optimization and search problems.…”

Section: Introductionmentioning

confidence: 99%

A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

et al. 2021

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Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.

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“…One key aspect of sports events is the ability to create a timetable in such a way that the logistical aspects are optimized and fair. This area has a history of 40 years, and in recent years, the number of articles written for this subject has increased dramatically, indicating that scientific interest in this field is increasing [2].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Mathematical Model in Sports Events Timetabling

Simorghi¹,

Bakhshandeh²

2017

MJMIE

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Events such as Olympics, Football World Cup and Golf and Tennis huge tournaments have always drawn the attention from fans and TV viewers. To obtain this attention many factors are involved. One the major reasons to attract fans is the time order of matches. Another factor is that prominent matches must be distributed over a time interval of the tournament. These make a problem which is called "Sport Events Timetabling". This article develops a MIP model to scheduling sport events. In this model, intrinsic constraints of sport events timetabling are considered. For example, it is possible to schedule group matches and matches which plays between two teams, simultaneously. At the end a case study is introduced and solved with the GAMS 9.32 software.

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Scheduling the English Football League with a Multi-objective Evolutionary Algorithm

Cited by 7 publications

References 12 publications

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

An Enhanced Mathematical Model in Sports Events Timetabling

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