ABSTRACT Quantum computing is poised to transform computational paradigms across science and industry. As the field evolves, it can benefit from established classical methodologies, including promising paradigms such as Transfer of Knowledge (ToK). This work serves as a brief, self‐contained reference for ToK, unifying its core principles under a single formal framework. We introduce a joint notation that consolidates and extends prior work in Transfer Learning and Transfer Optimisation, bridging traditionally separate research lines and enabling a common language for knowledge reuse. Building on this foundation, we classify existing ToK strategies and principles into a structured taxonomy that helps researchers position their methods within a broader conceptual map. We then extend key transfer protocols to quantum computing, introducing two novel use cases—reverse annealing and multitasking Quantum Approximate Optimization Algorithm (QAOA)—alongside a sequential Variational Quantum Eigensolver (VQE) approach that supports and validates prior findings. These examples highlight ToK's potential to improve performance and generalisation in quantum algorithms. Finally, we outline challenges and opportunities for integrating ToK into quantum computing, emphasising its role in reducing resource demands and accelerating problem‐solving. This work lays the groundwork for future synergies between classical and quantum computing through a shared, transferable knowledge framework.

This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System’s devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.

Many countries around the world have witnessed the progressive ageing of their population, giving rise to a global concern to respond to the needs that this process will create. Besides the changes in the productive schemes and the evolution of the healthcare resources to new models, the accessibility of pedestrians belonging to this age range is grasping an increasing interest in urban planning processes. This work presents pre-liminary results of a framework that combines graph modeling and meta-heuristic optimization to inform decision makers in urban planning when deciding how to regenerate urban spaces taking into account pedestrian accessibility for the older people in urban areas with difficult orography. The goal of the framework is to decide where to deploy urban elements (mechanical ramps, escalators and lifts), so that an indirect measure of accessibility is improved while also accounting for the economical investment of the installation. We exploit the versatility of multi-objective evolutionary algorithms to tackle the underlying optimization problem. Experimental results of a case study located in the city of Santander (Spain) show that the proposed framework can support urban planners when making decisions regarding the accessibility of the public space.

In multimodal optimization problems the main goal is to find as many global optima as possible by using a single search process. This type of optimization tasks emerges in many real-world scenarios in assorted fields including medicine, physics, and aerospace, among many others. However, addressing several multimodal optimization problems simultaneously has received little attention from the multitask optimization community to date. Even though solving different multimodal problems at the same time can largely benefit from the existing synergies among the modes of different tasks, this setup has been less studied than other optimization tasks. This work finds its inspiration in the incipient concepts of Evolutionary Multitasking and Multifactorial Optimization to propose a multifactorial Cellular Genetic Algorithm for solving multimodal optimization problems. Our designed algorithm expedites the search for the global optima of different problems at a time by including several algorithmic steps aimed at adapting the search itself as per the synergies found over the exploration of the problems' landscape. An extensive experimentation has been designed using 14 different functions from the CEC‘2013 competition on multimodal optimization benchmark. Besides evaluating the performance of the devised algorithm to retain the global optima of every function in the benchmark, we also conduct an analysis of the transfer of knowledge among such functions. Finally, we compare its performance to that of a winning proposal in this CEC‘2013 competition so as to reflect on the suitability of the multitasking paradigm to solve multimodal optimization tasks.

One of the most well-known problems in combinatorial optimization is the Vehicle Routing Problem (VRP). Significant research has been done around this problem in two different perspectives: investigating new solving approaches, and studying variants of VRP which take into consideration multiple restrictions and constraints. One of such versions is the Electric Vehicle Routing Problem (EVRP), whose main objective is to find the optimal route of a fleet of electric vehicles, taking into account the locations of charging stations and the battery consumption of the mobile units. The aim of this study is threefold: (a) to perform a brief literature review on meta-heuristic approaches applied to the EVRP, (b) to offer insights on the available data instances for this problem, and (c) to discuss on the results of an experimental benchmark aimed at comparing different meta-heuristic approaches over diverse EVRP instances, including the proposal and evaluation of a novel Ant Colony Optimization approach.

Evolutionary computation has largely exhibited its potential to complement conventional learning algorithms in a variety of machine learning tasks, especially those related to unsupervised (clustering) and supervised learning. It has not been until lately when the computational efficiency of evolutionary solvers has been put in prospective for training reinforcement learning models. However, most studies framed so far within this context have considered environments and tasks conceived in isolation, without any exchange of knowledge among related tasks. In this manuscript we present A-MFEA-RL, an adaptive version of the well-known MFEA algorithm whose search and inheritance operators are tailored for multitask reinforcement learning environments. Specifically, our approach includes crossover and inheritance mechanisms for refining the exchange of genetic material, which rely on the multilayered structure of modern deep-learning-based reinforcement learning models. In order to assess the performance of the proposed approach, we design an extensive experimental setup comprising multiple reinforcement learning environments of varying levels of complexity, over which the performance of A-MFEA-RL is compared to that furnished by alternative nonevolutionary multitask reinforcement learning approaches. As concluded from the discussion of the obtained results, A-MFEA-RL not only achieves competitive success rates over the simultaneously addressed tasks, but also fosters the exchange of knowledge among tasks that could be intuitively expected to keep a degree of synergistic relationship.

Quantum Computing is considered as the next frontier in computing, and it is attracting a lot of attention from the current scientific community. This kind of computation provides to researchers with a revolutionary paradigm for addressing complex optimization problems, offering a significant speed advantage and an efficient search ability. Anyway, Quantum Computing is still in an incipient stage of development. For this reason, present architectures show certain limitations, which have motivated the carrying out of this paper. In this paper, we introduce a novel solving scheme coined as hybrid Quantum Computing - Tabu Search Algorithm. Main pillars of operation of the proposed method are a greater control over the access to quantum resources, and a considerable reduction of non-profitable accesses. To assess the quality of our method, we have used 7 different Traveling Salesman Problem instances as benchmarking set. The obtained outcomes support the preliminary conclusion that our algorithm is an approach which offers promising results for solving partitioning problems while it drastically reduces the access to quantum computing resources. We also contribute to the field of Transfer Optimization by developing an evolutionary multiform multitasking algorithm as initialization method.

In recent years, Multifactorial optimization (MFO) has gained a notable momentum in the research community. MFO is known for its inherent capability to efficiently address multiple optimization tasks at the same time, while transferring information among such tasks to improve their convergence speed. On the other hand, the quantum leap made by Deep Q Learning (DQL) in the Machine Learning field has allowed facing Reinforcement Learning (RL) problems of unprecedented complexity. Unfortunately, complex DQL models usually find it difficult to converge to optimal policies due to the lack of exploration or sparse rewards. In order to overcome these drawbacks, pre-trained models are widely harnessed via Transfer Learning, extrapolating knowledge acquired in a source task to the target task. Besides, meta-heuristic optimization has been shown to reduce the lack of exploration of DQL models. This work proposes a MFO framework capable of simultaneously evolving several DQL models towards solving interrelated RL tasks. Specifically, our proposed framework blends together the benefits of meta-heuristic optimization, Transfer Learning and DQL to automate the process of knowledge transfer and policy learning of distributed RL agents. A thorough experimentation is presented and discussed so as to assess the performance of the framework, its comparison to the traditional methodology for Transfer Learning in terms of convergence, speed and policy quality, and the intertask relationships found and exploited over the search process.

Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization problems can be tackled simultaneously by performing a single search process. The main objective to achieve this goal efficiently is to exploit synergies between the problems (tasks) to be optimized, helping each other via knowledge transfer (thereby being referred to as Transfer Optimization). Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration. As such, EM approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has shown a remarkable success when dealing with multiple discrete, continuous, single-, and/or multi-objective optimization problems. In this work we propose a novel algorithmic scheme for Multifactorial Optimization scenarios - the Multifactorial Cellular Genetic Algorithm (MFCGA) - that hinges on concepts from Cellular Automata to implement mechanisms for exchanging knowledge among problems. We conduct an extensive performance analysis of the proposed MFCGA and compare it to the canonical MFEA under the same algorithmic conditions and over 15 different multitasking setups (encompassing different reference instances of the discrete Traveling Salesman Problem). A further contribution of this analysis beyond performance benchmarking is a quantitative examination of the genetic transferability among the problem instances, eliciting an empirical demonstration of the synergies emerged between the different optimization tasks along the MFCGA search process.

This paper presents a new artificial intelligence-based method to address the following problem: given an initial digital image (source image), and a modification of the image (mod image) obtained from the source through a color map and visual attributes assumed to be unknown, determine suitable values for color map and contrast such that, when applied to the mod image, a similar image to the source is obtained. This problem has several applications in the fields of image restoration and cleaning. Our approach is based on the application of a powerful swarm intelligence method called bat algorithm. The method is tested on an illustrative example of the digital image of a famous oil painting. The experimental results show that the method performs very well, with a similarity error rate between the source and the reconstructed images of only 8.37%.

We address the following problem: given an initial high-quality reference image and a variation of it, how to compute suitable values for color map and contrast such that, when applied to this variation, we get an image very similar visually to the reference image. This problem can be formulated as an optimization problem. Unfortunately, this leads to a continuous nonlinear optimization problem too difficult to handle by classical mathematical optimization techniques. To tackle this issue, we apply a powerful swarm intelligence method called cuckoo search algorithm. The method is tested on an illustrative example of a famous painting by artist Vincent Van Gogh. The experimental results show that the method performs very well, with a similarity error rate between the reference and the reconstructed images of only 5.73%. The method can be applied to any variation of the original painting regardless of its initial color map and contrast.

Abstract This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark.

This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.

Nowadays the widespread adoption of cryptocurrencies (also referred to as Altcoins) has universalized the access of the society to trading opportunities in alternative markets, thereby laying a rich substrate for the development of new applications and services aimed at easing the management of personal investment portfolios. When selecting how much to invest and in which asset it is often the case that multiple criteria conflict with each other within a single decision making process, which calls for efficient means to optimally balance such contradicting objectives. In this paper we report initial findings around the combination of Deep Learning (DL) models and Multi-Objective Evolutionary Algorithms (MOEAs) for allocating cryptocurrency portfolios. Technical rationale and details are given on the design of a stacked DL recurrent neural network, and how its predictive power can be exploited for yielding accurate ex ante estimates of the return and risk of the portfolio. These two objectives are complemented by a measure of the diversity of the investment. Results are presented and discussed with real cryptocurrency data, showcasing the potential of our technical approach to produce near-optimal portfolios by balancing the aforementioned objectives. Our study stimulates further research towards incorporating other factors in the design of predictive portfolios, such as the confidence of the DL model output.

Nowadays, cooperative game theory has been applied to many domains of human activities. In this study, the cooperative game concept needed for calculating Shapley value is used in solving global optimization. Precisely, the marginal contribution that an agent carries by joining a coalition is calculated as an increase in population diversity of coalition. This concept is incorporated into differential evolution and its self-adaptive variants jDE in order to show that distributing the monolithic population of solutions into more coalitions and their parallel evolution can improve the results of the original algorithms.

Computerized analysis of skin lesions is an important issue in information retrieval for medical imaging, as it helps human specialists to improve their decision-making for prompt and accurate diagnosis of melanoma and other skin diseases. A relevant task in this regard is border detection, which gives valuable information about some clinical features of skin lesions. This task is typically carried out manually by the dermatologists, leading to errors inherent to subjective diagnosis. In this paper, we address this problem by applying a modification of a powerful evolutionary computation method, the firefly algorithm. The modified algorithm is hybridized with a local search procedure for better performance. Experimental results on a benchmark of medical images of skin lesions show that this method outperforms classical mathematical methods for the instances in the benchmark and is very competitive and often superior to state-of-the-art techniques in the field in terms of numerical accuracy. We conclude that the approach is very promising and can be useful in real-world medical applications where speed is not a critical factor.

An improved solution for drug distribution is presented in this paper. It is divided into two parts: i) a multi-operator evolutionary algorithm in charge of calculating the initial delivery routes and ii) an ambient intelligence-based support system able to tracing the merchandise along the distribution route. The first one establishes the routes to be followed by the vehicles, based on a proposal of estimation of the travel times. The second one is formed by a system able to recognize and trace the drugs inside each vehicle. A laboratory experimentation has been conducted in order to demonstrate the adequacy of the route calculator. In addition, a field experimentation has been carried out by implementing the traceability system in a delivery van which of the drug distributor in the city of Bilbao.

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion.

We propose a multi-crossover and adaptive island based population algorithm (MAIPA). This technique divides the entire population into subpopulations, or demes, each with a different crossover function, which can be switched according to the efficiency. In addition, MAIPA reverses the philosophy of conventional genetic algorithms. It gives priority to the autonomous improvement of the individuals (at the mutation phase), and introduces dynamism in the crossover probability. Each subpopulation begins with a very low value of crossover probability, and then varies with the change of the current generation number and the search performance on recent generations. This mechanism helps prevent premature convergence. In this research, the effectiveness of this technique is tested using three well-known routing problems, i.e., the traveling salesman problem (TSP), capacitated vehicle routing problem (CVRP), and vehicle routing problem with backhauls (VRPB). MAIPA proves to be better than a traditional island based genetic algorithm for all these three problems.