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The aim of this paper is to provide a review of recent developments in the application of swarm intelligence to robotics.

This paper initially considers swarm intelligence and then discusses its application to robotics through reference to a number of recent research programmes.

Based on the principles of swarm intelligence, which is derived from the swarming behaviour of biological entities, swarm robotics research is widespread but still at an early stage. Much aims to gain an understanding of biological swarming and apply it to autonomous, mobile multi‐robot systems. European activities are particularly strong and several large, collaborative projects are underway. Research in the USA has a military bias and much is funded by defence agencies.

The paper provides an up‐to‐date insight into swarm robot research and development.

Parameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification.

Swarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young’s modulus of a concrete gravity dam and a concrete arch dam.

Numerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency.

Fireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.

The purpose of this chapter is to review our findings related to the question “Do outcomes related to dynamic assessment on a cognitive measure predict reading growth?” Our discussion related to the predictive validity of such procedures focused on outcomes related to a battery of memory and reading measures administered over a three-year period to 78 children (11.6 years) with and without reading disabilities (RD). Working memory (WM) tasks were presented under initial, gain, and maintenance testing conditions. The preliminary results suggested that maintenance testing conditions were significant moderators of comprehension and vocabulary growth, whereas probe scores and gain testing conditions were significant moderators of nonword fluency growth. Overall, the results suggested that the dynamic assessment of WM added significant variance in predicting later reading performance.

The purpose of this paper is to identify and analyse the typology of employee branding in an airline company using fuzzy c-means (FCM) clustering to improve the quality of employee brand (EB).

Data were collected from employees of Air India, Chennai division, using a questionnaire and analysed using FCM to find the optimum cluster number. The nature of each cluster was analysed to know its type.

The results prove the presence of four types of EB, namely, all-stars, injured reserves, rookies and strike-out kings in the aviation company. It is proven that employees in all-star have high level of knowledge of the desired brand (KDB) and psychological contract (PC), those in injured reserves have high KDB and low PC, rookies have low KDB and high PC and strike-out kings have low KDB and PC.

The results of this study are limited to the Air India employees. This study contributes to employee branding by empirically substantiating the proposed typology using FCM. It proposes the need to analyse organisations individually before comparisons.

The management must focus on the quality of training and development programmes to enhance the position of rookies and strike-out kings. It must also receive regular feedback from injured reserves and strike-out kings to evaluate their perception of PC.

This is the first paper to empirically prove the typology of employee branding and to implement FCM in clustering employees for enhancing the EB’s quality.

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms.

Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm).

Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it.

As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

This paper aims to approach the issue of premium offers in Italy by discussing the case study of Fabbri, a firm operating since 1905 in the business of liqueurs, syrups and semi-manufactured products for ice cream.

The research takes into analysis three marketing schemes, all related to direct premium promotions, adopted by Fabbri at various times during the twentieth century. The evolution of the company’s marketing strategy is outlined drawing on several types of sources: archive documents, posters and labels and audiovisual material. It is analysed in the socio-economic and legal context of twentieth century Italy, and in comparison with premium offers in the USA and Europe.

The study argues that direct premium may represent a long-lasting and efficient marketing strategy when a firm is able to adapt it to a context that changes over time. Fabbri not only used premium offers to launch its products but also to consolidate its brand image.

By showing that innovative promotions are not necessarily connected to large firms, Fabbri’s case suggests that further research should be carried out to outline marketing policies carried out by small to medium enterprises.

Much has been written on premium offers in the USA and in Europe, but very little on such types of promotions in Italy, especially with reference to direct premiums. This study fills this gap and documents that a small family-owned firm was able to carry out innovative marketing policies as far as in the 1920s.

Manufacturing firms are always faced with the problem of promoting operational performance and labor‐force management. The utilization of human resources is closely correlated with operations and production performance. This study investigates the correlation between human resource management (HRM) and business performance of large‐scale manufacturing firms in Taiwan. First, 16 subjects of HRM are designed to survey the importance level and achievement level of HRM by the sample firms. Productivity indices are also defined to measure business performance. Based on the survey, four critical HRM factors including 12 subjects are extracted by factor analysis. The difference between importance level and achievement level of subjects contained in each factor is examined. Furthermore, considering importance and achievement levels of HRM as features, fuzzy clustering analysis is employed to categorize the firms into four patterns. With various HRM characteristics, each pattern has different business performance in terms of productivity. Using a pattern approach, these findings can aid the firms in each pattern to improve their productivity by improving their HRM strategies.

Since a firm’s management performance can be evaluated in terms of financial ratios, efficient management using financial factors is proposed as the key element for upgrading a firm’s productivity. Investigates productivity in terms of certain financial factors of large‐scale manufacturing firms in Taiwan. First determines several influential financial factors using factor analysis. Based on these factors, employs fuzzy clustering approaches to categorize the manufacturing firms into several patterns with distinct characteristics of financial factors. Using the characteristics of productivity and financial factors for each pattern, makes two kinds of analysis, and proposes some suggestions to improve the firms’ productivity.

Purpose – To propose a pattern analysis method to help firms rectify weaknesses of production management (PM) and thus promote their business performance. Design/methodology/approach – Total factor productivity and the associated partial productivity indices are defined, and four kinds of production planning ranges, i.e. long‐range planning, medium‐range planning, short‐range planning, and execution, are defined based on 14 PM issues. A fuzzy clustering approach is applied to group the sampled firms into several patterns based on the achievement degrees of production planning in order to investigate the particular characteristics of each pattern. Findings – After analyzing the productivity characteristics of each pattern, the correlation between productivity and production management can be determined. In this study, the business performance seems to be not completely correlated with the achievements of production management, since moderate production planning can provide optimal business performance. Research limitations/implications – The patterns produced from the proposed approaches depend on the sampled data set. A solid sampling method is important to this study. Practical implications – The sampled data are collected from the top 50 large‐scale manufacturing firms in Taiwan. The results obtained from this paper may not be consistent with the situations in the other countries. Originality/value – Referring to the findings from each pattern, a firm can further investigate its position in the industry to find ways of increasing its competitiveness.

Proposes an approach to improving production management (PM) of a firm through examining its resource utilization and product competence. To understand the status of PM in Taiwan, evaluates the achievement levels of 14 production management subjects from 50 large‐scale manufacturing firms. The subjects are further classified into four planning ranges of PM. Then productivity measures of these firms for resource utilization and product competence are determined. Two groups of patterns with distinct PM characteristics are found through a fuzzy clustering analysis based on resource utilization and product competence. Significant correlation has been found between resource utilization and product competence and PM. The PM patterns with good performance in terms of resource utilization and product competence are considered targets for firms of other patterns. The ways of adjusting PM for these firms of other patterns are then described in terms of the characteristics of the target patterns.