Abstract This study provides a Systematic Literature Review on the application of Artificial Intelligence algorithms in the primary sector of olive cultivation. It compiles and analyses a collection of studies that leverage AI to enhance the efficiency and sustainability of olive production, maintenance, and harvesting processes. In this study, 43 papers were reviewed from the databases IEEE, Scopus, and Web of Science through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. This research aims to identify AI applications in the primary olive growing sector. The findings highlight a significant trend toward adopting advanced AI techniques, particularly Deep Learning algorithms such as Convolutional Neural Networks, for many tasks ranging from cultivar identification and foliar disease classification to crop yield forecasting with high accuracies.

Due to the increasing of life expectancy in the developed countries, the demand for home health care services is growing dramatically. Usually, home services are planned manually and lead to various optimization problems in their activities. In this sense, health units are confronted with appropriate scheduling which may contain multiple, often conflicting, objectives such as minimizing the costs related to the traveling distance while minimizing the traveling time. In order to analyze and discuss different trade-offs between these objectives, it is proposed a multi-objective approach to home health care scheduling in which the problem is solved using the Tchebycheff method and a Genetic algorithm. Different alternative solutions are presented to the decision maker that taking into account his/her preferences chooses the appropriate solution. A problem with real data from a home health care service is solved. The results highlight the importance of a multi-objective approach to optimize and support decision making in home health care services. Moreover, this approach provides efficient and good solutions in a reasonable time.

Science and Technology Publications, Lda. All rights reserved. In this paper, a methodology to solve constrained multi-objective problems is presented, using an Augmented Lagrangian technique to deal with the constraints and the Augmented Weighted Tchebycheff method to tackle the multi-objective problem and find the Pareto Frontier. We present the algorithm, as well as some preliminary results that seem very promising when compared to previous state-of-the- art work. As far as we know, the idea of incorporating an Augmented Lagrangian in multi-objective optimization is rarely used so, the obtained results are very encouraging to pursuit further in this line of investigation, namely with the tuning of the Augmented Lagrangian parameters as well as testing other algorithms to solve the subproblems or to handle the multi-objective problems. It is also our intention to investigate the resolution of problems with three or more objectives.

The high costs associated with the design of wastewater treatment plants (WWTPs) motivate the research in the area of modelling their construction and the water treatment process optimization. This work addresses different methodologies, which are based on defining and simultaneously optimizing several conflicting objectives, for finding the optimal values of the state variables in the plant design. We use an evolutionary many-objective optimization algorithm with clustering-based selection that proved effective in handling challenging optimization problems with a large number of objectives. The obtained results are promising and with physical meaning. It is shown that the overall WWTP design can be improved by coming up with appropriate formulation of the optimization problem and solving approach.

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

Requirement prioritization is a process that allows selection of the “key” candidate requirements, the ones that are the most important for the construction of quality and cost-controlled software. Requirement prioritization brings certain issues and challenges related with the different stakeholders involved in the project, as well as with the prioritization techniques used, which differ in procedures, criteria and metrics. This manuscript compares two multi-criteria decision methods (MCDM), AHP and ELECTRE I, seeking to justify which one is the most feasible in the requirement prioritization process of a real-world case study. To accomplish this aim, several criteria were used to compare the applicability and performance of both MCDMs. In order to reflect reality as close as possible, several stakeholders, including software professionals directly related to the case study, were involved. The results confirm the intuition that ELECTRE I is more easily applicable than AHP. ELECTRE I is subject to fewer mistakes in comparisons of the requirements than the AHP method, as these are carried out differently. In fact, due to its inherent complexity, AHP becomes even impractical in software projects with a large number of requirements.

Generating biped locomotion in robotic platforms is hard. It has to deal with the complexity of the tasks which requires the synchronization of several joints, while monitoring stability. Further, it is also expected to deal with the great heterogeneity of existing platforms. The generation of adaptable locomotion further increases the complexity of the task. In this paper, Genetic Programming (GP) is used as an automatic search method for motion primitives of a biped robot, that optimizes a given criterion. It does so by exploring and exploiting the capabilities and particularities of the platform. In order to increase the adaptability of the achieved solutions, feedback pathways were directly included into the evolutionary process through sensory inputs.

In this paper, we propose an evolutionary algorithm for handling many-objective optimization problems called MyO-DEMR (many-objective differential evolution with mutation restriction). The algorithm uses the concept of Pareto dominance coupled with the inverted generational distance metric to select the population of the next generation from the combined multi-set of parents and offspring. Furthermore, we suggest a strategy for the restriction of the difference vector in DE operator in order to improve the convergence property in multi-modal fitness landscape.

This paper presents a biped gait optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. CPGs are modeled as autonomous differential equations, that generate the necessary limb movements to perform the walking gait of a biped robot. The search for the best set of CPG parameters is optimized by considering multiple objectives and according to a staged evolution. A sensitivity analysis is used to evaluate the relationship between objectives, objectives and parameters, and allows to determine the functional meanings of the parameters. This resulting functional analysis enables to verify which parameters are relevant to the motor behaviors. The simulation results show the effectiveness of the proposed approach. The different obtained walking gait solutions correspond to different trade-offs between the objectives.

Optimization with stochastic algorithms has become a relevant approach, specially, in problems with complex search spaces. Due to the stochastic nature of these algorithms, the assessment and comparison is not straightforward. Several performance measures have been proposed to overcome this difficulty. In this work, the use of performance profiles and an analysis integrating a trade-off between accuracy and precision are carried out for the comparison of two stochastic algorithms. Traditionally, performance profiles are used to compare deterministic algorithms. This methodology is applied in the comparison of two stochastic algorithms - genetic algorithms and simulated annealing. The results highlight the advantages and drawbacks of the proposed assessment.

This paper presents a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, four conflicting objectives are considered, simultaneously: minimize the body vibration, maximize the velocity, maximize the wide stability margin and maximize the behavioral diversity. The results of NSGA-II for this multi-objective problem are discussed. The effect of the inclusion of a behavioral diversity objective in the system is also studied in terms of the walking gait achieved. The experimental results show the effectiveness of this multi-objective approach. The several walking gait solutions obtained correspond to different trade-off between the objectives.

Decision making process in multiobjective problems becomes more difficult in the presence of a large number of objectives and approximations to the Pareto optimal solutions. Consequently, the representation and visualization of the Pareto optimal frontier is not simple. Therefore, it is not clear for the decision maker the trade-off between the different alternative solutions. Thus, this creates enormous difficulties when choosing a solution from the Pareto-optimal set and constitutes a central question in the process of decision making. A methodology based on Principal Component Analysis and Biplot graphical representations is proposed to retrieve information from approximations to the Pareto optimal set and associations between objectives. Thus, taking into account biplot representations, offline objective reduction can be performed as well as the identification of proximities between solutions. Some examples and datasets with different number of objectives have been studied in order to evaluate the process of decision making through these methods. Results indicate that this statistical approach can be a valuable tool on decision making in multiobjective optimization.

Software inspections allow finding and removing defects close to their point of injection and are considered a cheap and effective way to detect and remove defects. A lot of research work has focused on understanding the sources of variability and improving software inspections performance. In this paper we studied the impact of inspection review rate in process performance. The study was carried out in an industrial context effort of bridging the gap from CMMI level 3 to level 5. We supported a decision for process change and improvement based on statistical significant information. Study results led us to conclude that review rate is an important factor affecting code inspections performance and that the applicability of statistical methods was useful in modeling and predicting process performance.

In multiobjective optimization there is often the problem of the existence of a large number of objectives. For more than two objectives there is a difficulty with the representation and visualization of the solutions in the objective space. Therefore, it is not clear for the decision maker the trade-off between the different alternative solutions. Thus, this creates enormous difficulties when choosing a solution from the Pareto-optimal set and constitutes a central question in the process of decision making. Based on a statistical method, Principal Component Analysis, the problem of reduction of the number of objectives is addressed. Several test examples with different number of objectives have been studied in order to evaluate the process of decision making through these methods. Preliminary results indicate that this statistical approach can be a valuable tool on decision making in multiobjective optimization.

The head shaking that results from robot locomotion is important because it difficults stable image acquisition and the possibility to rely on that information to act accordingly, for instance, to achieve visually-guided locomotion.

Almost all approaches to multiobjective optimization are based on Genetic Algorithms (GAs), and implementations based on Evolution Strategies (ESs) are very rare. Thus, it is crucial to investigate how ESs can be extended to multiobjective optimization, since they have, in the past, proven to be powerful single objective optimizers. In this paper, we present a new approach to multiobjective optimization, based on ESs. We call this approach the Multiobjective Elitist Evolution Strategy (MEES) as it incorporates several mechanisms, like elitism, that improve its performance. When compared with other algorithms, MEES shows very promising results in terms of performance.

Almost all approaches to multiobjective optimization are based on genetic algorithms, and implementations based on evolution strategies (ESs) are very rare. In this paper, a new approach to multiobjective optimization, based on ESs, is presented. The comparisons with other algorithms indicate a good performance of the Multiobjective Elitist Evolution Strategy.