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The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems.

This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population.

The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm.

The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper.

The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives.

This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

While today's customer steadily adapts to various modes of shopping, their beliefs around fluency through each shopping channel, and personal factors such as income level, can impact their intention to patronage or purchase from omnichannel department stores. Hence, this study analysed the customers of omnichannel fashion department stores, using perceived fluency and income as indirect factors that help understand customers' patronage intention and purchase intention.

The overarching framework for this research is the theory of reasoned action, in which patronage and purchase intentions represent the specific likelihood-of-performance behaviours. A Seemingly Unrelated Regression model was empirically used to analyse the relationships between generational cohorts, income, and perceived channel fluency and the behaviours that lead to patronage intention and purchase intention. Researchers conducted a survey among 552 omnichannel fashion department store consumers to examine today's retail environment.

The results of this study suggest that (1) consumers between the ages of 50 and 69 years, including older Generation X and younger Baby Boomers, who earn between $60,000 and $79,999 in annual salary show a significantly positive relationship with both patronage and purchase intentions through perceived fluency and (2) consumers between the ages of 38 and 49 years, including older Millennials and younger Generation X, who earn between $80,000 and $99,999 in annual salary show a significantly positive relationship with purchase intention through perceived fluency

This study analyses correlations between a generational cohort, perceived fluency as moderated by income and the relationship between these variables and customers' patronage and purchase intentions, which has not been studied before.

The purpose of this paper is to explain in detail the optimization of the sensitivity versus the power consumption of a pressure microsensor using multi-objective genetic algorithms.

The tradeoff between sensitivity and power consumption is analyzed and the Pareto frontier is identified by using NSGA-II, AMGA-II and ɛ-MOEA methods.

Comparison results demonstrate that NSGA-II provides optimal solutions over the entire design space for spread metric analysis, and AMGA-II is better for convergence metric analysis.

This paper provides a new multiobjective optimization tool for the designers of low power pressure microsensors.

The period of transition to democracy in which Generation Z members in Bulgaria grew up was characterised by profound changes in the economic and social system of the country, with frequent episodes of chaos and instability resulting in a long-term demographic decline. At the same time, the years of their adolescence have been marked by globalisation processes and the rapid development of digital technologies opening countless opportunities for work, study and travel to this group of young people.

Although research on Generation Z in Bulgaria is scarce, in this chapter, we have attempted to draw a portrait of the typical representative of the young generation based on the results of a couple of empirical surveys. Decreasing social orientation, less focus on sustaining interpersonal relationships and lower self-confidence and initiative are among our most remarkable observations making this generation of young people rather different than previous ones. Furthermore, Generation Z members were found to be quite demanding at work requiring stress-free working conditions, good work–life balance, opportunities for competency development and adequate pay from employers. This certainly creates a serious challenge for Bulgarian employers who might have to change their standard human resource practices in order to attract and retain the potential of this new group of employees. Generation Z members could be a source of innovation, meaningfulness and flexibility for the Bulgarian labour market and because of that they certainly need to be studied in more detail.

In principle, it is possible to apply genetic algorithms (GAs) to the solution of inverse problems in the simulation of manufacturing processes. In this context, an inverse problem can be stated as ‘knowing the desired output of a process, what combination of process parameters are required for its achievement?’. Since the simulation of many processes requires the simulation of thermal, solids and/or fluids problems, the application of GAs to inverse process modelling depends on their ability to solve a wide range of inverse field problems. This paper has two major objectives: (a) to demonstrate the application of a GA to a simple inverse thermal field problem, and (b) to compare its performance against a relatively mature technique for the solution of such problems. The results of this study indicate that, despite the relatively large computational cost of GAs, their accuracy and robustness warrants further investigation of their performance in more demanding applications.

The purpose of this paper is to address the subject of work attitude drivers within the current scenario considering two influencing factors, culture and generation. Both membership of a particular generation and membership of a particular culture can affect individuals' work attitudes. The study considers these two factors in order to analyse five dimensions that are sources of work attitudes: life project, professional ethics, and attitude towards authority, leadership and commitment to the company.

Drawing upon a sample of almost one thousand people from various Ibero‐American countries, the results show significant differences between generations and cultures, particularly when focusing on the life project.

Among its conclusions, the paper points out that Latin America cannot be viewed as a homogenous whole in terms of individual work attitude. On the contrary, it is characterised by a significant degree of national diversity and managers should take this into account when designing initiatives to improve employee motivation.

The contribution that this paper seeks to make is to include the cross‐cultural perspective in the study of attitudes towards work, which has received less attention in previous studies.

The purpose of this study is to examine the differences in preferred work-related attributes across generational cohorts. Specific focus is given to investigating whether millennials have different preferences than previous generations.

This study uses cross-sectional survey data of 300 employees of a large firm in the southwest USA. Conjoint analysis is used to collect employee responses that are then subjected to analysis of variance (ANOVA).

The results of this study demonstrate that employees from different generations have significantly different preferences on four work-related attributes: job security, potential for advancement, work/life balance and company leadership.

This study extends the generational cohort theory by employing psychological contract theory to discover and explain significant differences in preferences for varying work-related attributes for different generations. Like much cross-sectional research, these findings have to be validated and generalized.

Firms can use the results of this study to help understand how different generations value different work-related attributes, thus helping improve employee satisfaction and retention.

The originality of this research lies in its very unique approach, conjoint analysis, to be one of the first studies to test empirically the preferences for work-related attributes across generational cohorts. It lays the foundation for future research to expand upon while also giving practicing managers a useful tool to understand the needs of their employees.

The purpose of this study is to understand the differences in motivations, advantages, disadvantages and time of multiple-major awareness among students who pursue multiple majors based on a set of defined characteristics. The student characteristics of interest included race, gender, financial aid status, class standing, transfer status, first-generation status and the number of majors.

The authors administered a survey instrument to a random sample of multiple-major undergraduate students to gauge the prevalence of motivations, advantages, disadvantages and time of multiple-major awareness themes developed during individual interviews.

Statistical analysis revealed significant differences among multiple-major students based on characteristics of interest. Results discussed at length include transfer students deriving higher levels of motivation from degree practicality than nontransfer students and students who receive financial aid indicating multiple passions as a primary motivation more frequently than students not receiving financial aid. Similar differences between male and female students are uncovered relating to perceived advantages of diverse interactions and experiences and increased balance, as well as perceived disadvantages of time commitment and ability to grow professionally. Finally, first-generation students learned about multiple majoring later than non-first-generation students.

This study builds on previous research regarding multiple-major students, an understudied yet important population in higher education. Additionally, it delves deeper by exploring differences in this population by student characteristics.

In this paper, a modification strategy is proposed for the particle swarm optimization (PSO) algorithm. The strategy adds an adaptive scaling term into the algorithm, which aims to increase its convergence rate and thereby to obtain an acceptable solution with a lower number of objective function evaluations. Such an improvement can be useful in many practical engineering optimizations where the evaluation of a candidate solution is a computationally expensive operation and consequently finding the global optimum or a good sub‐optimal solution with the algorithm is too time consuming, or even impossible within the time available. The modified PSO algorithm was empirically studied with a suite of four well‐known benchmark functions, and was further examined with a practical application case, a neural‐network‐based modeling of aerodynamic data. The numerical simulation demonstrates that the modified algorithm statistically outperforms the original one.

In this work, the authors show the performance of the proposed diploid scheme (a representation where each individual contains two genotypes) with respect to two dynamic optimization problems, while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms (EAs) to standard EAs. The paper aims to discuss this issue.

In the proposed diploid representation of EA, each individual possesses two copies of the genotype. In order to convert this pair of genotypes to a single phenotype, each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype. In order to provide a fair and objective comparison, the authors make sure to compare populations which contain the same amount of genetic information, where the only difference is the arrangement and interpretation of the information. The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position.

For small fitness landscapes the haploid (standard) and diploid algorithms perform comparably and are able to find the global optimum very quickly. However, as the search space increases, rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum. Since both algorithms use the same amount of genetic information, it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better.

The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele (value) in the vector of values that is the genotype, dominance is adopted across the entire genotype in relation to its homologue. As a result, this representation can be extended across any alphabet, for any optimization function.