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This study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously.

TTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions.

The computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm.

MOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence.

Metaheuristic algorithms have been commonly used as an optimisation tool in various fields. However, optimisation of real-world problems has become increasingly challenging with to increase in system complexity. This situation has become a pull factor to introduce an efficient metaheuristic. This study aims to propose a novel sport-inspired algorithm based on a football playing style called tiki-taka.

The tiki-taka football style is characterised by short passing, player positioning and maintaining possession. This style aims to dominate the ball possession and defeat opponents using its tactical superiority. The proposed tiki-taka algorithm (TTA) simulates the short passing and player positioning behaviour for optimisation. The algorithm was tested using 19 benchmark functions and five engineering design problems. The performance of the proposed algorithm was compared with 11 other metaheuristics from sport-based, highly cited and recent algorithms.

The results showed that the TTA is extremely competitive, ranking first and second on 84% of benchmark problems. The proposed algorithm performs best in two engineering design problems and ranks second in the three remaining problems.

The originality of the proposed algorithm is the short passing strategy that exploits a nearby player to move to a better position.

Lesson 1: Regions still matter and localization helps. Lesson 2: Innovate before your competence gets commoditized or neutralized by a counter strategy. Lesson 3: There is no ONE right strategy; do proper SWOT to determine your strategy. Lesson 4: Adapt- modify your strategy as per the situation Lesson 5: Surprise element can be a big source of competitive advantage.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.

Based on Spain's recent achievement as soccer World Cup winner, the purpose of this paper is to draw lessons for business leaders.

This paper is a reflection, from a strategic viewpoint, on the events surrounding the championship.

Ten general lessons are drawn for business leaders, entrepreneurs, family business owners, and managers in how they interact with customers and employees.

The paper's value lies in using a high visibility sports event to draw lessons for businesses in a practice‐oriented note.

This paper aims to illustrate how network analysis can be used to assess the group relationships within a professional football team, starting from the assumption that team results depend, at least in part, on the interaction between team members on the pitch. Elaborating an evaluation model of team relationships can help management in making conscious choices with regards to footballer assessment, selection and acquisition.

The methodology is based on a qualitative/quantitative approach. Data have been acquired through direct observation. UCINET 6.4 software was used to elaborate the data.

An empirical observation was carried out according to the network analysis applicative process, through the analysis of a UEFA Champions League match. The objective was to illustrate the potential of network analysis to assess football team relationships and identify a system of quantitative key indicators, which can be used to elaborate a framework for evaluating the relationships in professional football teams.

This model means that it is possible to analyse elements such as the group members’ attitude towards cooperation, providing an evaluation tool for membership relationships that have not yet been expressed through quantitative indicators, as these indicators are relevant in the development of football game tactics.