As a major metropolitan city, London faces persistent road congestion and severe air pollution. To address these issues, static electronic road pricing (ERP) models have been implemented. While effective, these are inherently limited in flexibility. This paper explores dynamic ERP models to improve upon static pricing by minimizing air pollution and traffic congestion within the Congestion Charge Zone. The problem is formulated as a multi-stakeholder multi-objective optimization problem, incorporating the perspectives of three stakeholders—the government, vehicle owners, and environmental organizations—and three objectives: air pollution, traffic congestion, and price. The NSGA-II optimization algorithm was applied on a representative day and demonstrated substantial improvements. The concentration of PM$ {}_{2.5} $—the more harmful pollutant—was reduced by up to 23%, while NO2 levels fell by 2–3%. Traffic flow, used as a proxy for congestion, decreased by approximately 3–4% during peak hours. These improvements were achieved with only a modest increase in the mean price to £12.51 (from a baseline of £11.50), with a standard deviation of £1.59 and a variance of £2.43 across hourly prices. These results suggest that targeted dynamic pricing—when aligned with environmental and behavioural incentives—can deliver measurable gains in urban air quality and congestion without imposing a significant cost burden on drivers. A core novelty of this work lies in its practical, stakeholder-inclusive problem formulation. While the approach assumes infrastructure for automated price deduction and routing, this limitation can be addressed in future work through advances in vehicle–infrastructure communication systems.

Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.

This paper addresses the problem of optimal mechanisms design, for the geometric structure and control parameters of mechanisms with complex kinematics, which is one of the most intricate problems in contemporary robot modeling. The problem is stated by means of task requirements and performance constraints, which are specified in terms of the end-effector's position and orientation to accomplish the task. Usually, this problem does not fulfill the characteristics needed to use gradient-based optimization algorithms. In order to circumvent this issue, we introduce case studies of optimization models using evolutionary algorithms (EAs), which deal with the concurrent optimization of both: structure and control parameters. We define and review several optimization models based on the workspace, task and dexterity requirements, such that they guarantee an adequate performance under a set of operating and joint constraints, for a Delta parallel manipulator. Then, we apply several methodologies that can approximate optimal designs. Additionally, we compare the EAs with a quasi-Newton method (the BFGS), in order to show that the last kind of methods is not capable of solving the problem if the initial point is not very close to a local optimum. The results provide directions about the best state-of-the-art EA for addressing different design problems.

A platform for experimenting with population-based design exploration algorithms is presented, called Dexen. The platform has been developed in order to address the needs of two distinct groups of users loosely labeled as researchers and designers. Whereas the researchers group focuses on creating and testing customized toolkits, the designers group focuses on applying these toolkits in the design process. A platform is required that is scalable and extensible: scalable to allow computationally demanding population-based exploration algorithms to be executed on distributed hardware within reasonable time frames, and extensible to allow researchers to easily implement their own customized toolkits consisting of specialized algorithms and user interfaces. In order to address these requirements, a three-tier client–server system architecture has been used that separates data storage, domain logic, and presentation. This separation allows customized toolkits to be created for Dexen without requiring any changes to the data or logic tiers. In the logic tier, Dexen uses a programming model in which tasks only communicate through data objects stored in a key-value database. The paper ends with a case study experiment that uses a multicriteria evolutionary algorithm toolkit to explore alternative configurations for the massing and façade design of a large residential development. The parametric models for developing and evaluating design variants are described in detail. A population of design variants are evolved, a number of which are selected for further analysis. The case study demonstrates how evolutionary exploration methods can be applied to a complex design scenario without requiring any scripting.

This paper applies a social learning model to the optimal consumption rule of Allen and Carroll [Macroeconomic Dynamics 5(2001), 255–271] and delivers convincing convergence dynamics toward the optimal rule. These findings constitute a significant improvement over previous results in the literature, in terms of both speed of convergence and parsimony of the learning model. The learning model exhibits several appealing features: it is frugal, is easy to apply to a various range of learning objectives, and requires few procedures and little information. Particular care is given to behavioral interpretation of the modeling assumptions in light of evidence from the fields of psychology and social science. Our results highlight the need to depart from the genetic metaphor, and account for intentional decision-making, based on agents' relative performances. By contrast, we show that convergence is strongly hindered by exact imitation processes, or random exploration mechanisms, which are usually assumed when modeling social learning behavior. Our results suggest a method for modeling bounded rationality, which could be interestingly tested in a wide range of economic models with adaptive dynamics.

The paper presents the continuation of the author's research on ship track planning by means of Evolutionary Algorithms (EA). The presented method uses EA to search for an optimal set of safe tracks for all ships involved in an encounter. Until now the method assumed good visibility – compliance with standard rules of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS, 1972). However, in restricted visibility, when Rule 19 applies instead of Rules 11 to 18, the problem is a different one. Therefore this paper introduces the extended method, with a focus on compliance with Rule 19 and its implications. It includes descriptions of detecting, penalizing and eliminating violations of Rule 19. The method has been implemented and the paper contains sample results of computer simulation tests carried out for ship encounters in restricted visibility in both open and restricted waters. They confirm the effectiveness of the chosen approach and suggest that the method could be applied in on board decision support systems.

This paper addresses the dimensional-synthesis-based kineto-elastostatic performance optimization of the delta parallel mechanism. For the manipulator studied here, the main consideration for the optimization criteria is to find the maximum regular workspace where the robot delta must posses high stiffness and dexterity. The dexterity is a kinetostatic quality measure that is related to joint's stiffness and control accuracy. In this study, we use the Castigliano's energetic theorem for modeling the elastostatic behavior of the delta parallel robot, which can be evaluated by the mechanism's response to external applied wrench under static equilibrium. In the proposed formulation of the design problem, global structure's stiffness and global dexterity are considered together for the simultaneous optimization. Therefore, we formulate the design problem as a multi-objective optimization one and, we use evolutionary genetic algorithms to find all possible trade-offs among multiple cost functions that conflict with each other. The proposed design procedure is developed through the implementation of the delta robot and, numerical results show the effectiveness of the proposed design method to enhancing kineto-elastostatic performance of the studied manipulator's structure.

The paper presents the continuation of the author's research on Evolutionary Sets of Safe Ship Trajectories (ESoSST) methodology. In an earlier paper (Szlapczynski, 2011) the author described the foundations of this methodology, which used Evolutionary Algorithms (EA) to search for an optimal set of safe trajectories for all the ships involved in an encounter. The methodology was originally designed for open waters or restricted waters when only the standard Convention on the International Regulations for Preventing Collisions at Sea (COLREGS, 1972) rules apply. However, within Traffic Separation Schemes (TSS), where additionally Rule 10 of COLREGS applies, the problem is much more complex and a new solution is needed. This paper introduces the extended ESoSST methodology, with a focus on changes that have to be made to obey Rule 10 and fully support TSS. These changes include detecting and penalizing TSS violations, as well as the pre-processing phase (generating the initial population, which includes predefined TSS-compliant tracks). The methodology has been designed for possible application in Vessel Traffic Service (VTS) centres. Its new mechanisms are presented with details. The examples are included of the results of the computer simulation tests carried out for the Gulf of Gdansk TSS to illustrate the methodology's effectiveness and functional scope.

This paper focuses on computer-based generative methods for layout problems in architecture and urban planning with special regard for the hierarchical structuring of layout elements. The generation of layouts takes place using evolutionary algorithms, which are used to optimize the arrangement of elements in terms of overlapping within a given boundary and the topological relations between them. In this paper, the approach is extended by a data structure that facilitates the hierarchical organization of layout elements making it possible to structure and organize larger layout problems into subsets that can be solved in parallel. An important aspect for the applicability of such a system in the design process is an appropriate means of user interaction. This depends largely on the calculation speed of the system and the variety of viable solutions. These properties are evaluated for hierarchical as well as for nonhierarchical structured layout problems.

The paper introduces a new method of solving multi-ship encounter situations for both open waters and restricted water regions. The method, called evolutionary sets of safe trajectories, combines some of the assumptions of game theory with evolutionary programming and aims to find optimal sets of safe trajectories of all ships involved in an encounter situation. In a two-ship encounter situation it enables the operator of an onboard collision-avoidance system to predict the most probable behaviour of a target and to plan the own manoeuvres in advance. In a multi-ship encounter the method may be used to help an operator of a VTS system to coordinate the manoeuvres of all ships. The paper contains a detailed description of collision-avoidance operators used by the evolutionary method and simulation examples of the method's results for digital maps.

We propose a computational method for producing novel constructs that fall within an existing design or artistic style. The method is based on evolutionary algorithms, and we discuss related knowledge representation issues. We then present an implementation of this method that we used in order to imitate the style of the Dutch painter Mondrian. Finally, we explain and give the results of a cognitive experiment designed to determine the effectiveness of the method, and provide a discussion of these results.

A technique for open-loop minimum time planning of time-histories of control torques for robotic manipulators subject to constraints on the control torques using evolutionary algorithm is presented here. Planning is carried out in joint space of the manipulator and the path is represented as a string of via-points connected by cubic spline polynomial functions. Repeated path modification is done by using the evolutionary algorithm to search for a time-optimal path. Time taken to traverse over a particular path is calculated by reducing the dynamic equations of motion over that path in terms of a path parameter and then calculating the time optimal-control over that path.

This paper presents a migration strategy for a set of mobile agents (MAs) in order to satisfy customers' requests in a transport network, through a multimodal information system. In this context, we propose an optimization solution which operates on two levels. The first one aims to constitute a set of MAs building their routes, called Workplans. At this level, Workplans must incorporate all nodes, representing information providers in the multimodal network, in order to explore it completely. Thanks to an evolutionary approach, the second level must optimize nodes selection in order to increase the number of satisfied users. The assignment of network nodes to the required services must be followed by a Workplan update procedure in order to deduce final routes paths. Finally, simulation results are mentioned to invoke the different steps of our adopted approach.

The paper provides a solution to the current problem of adaptation in case-based design. The aim of this project is to develop a case-based model of design, using an evolutionary approach, for the adaptation of previously stored design solutions. It is argued that such a knowledge-lean methodology is more general in its applicability than conventional case-based design. A prototypical example in the context of 2-D spatial design of houses is used to test the efficacy and efficiency of this approach.

In this paper, the design of geometrical shapes of function carriers and their layout in given space is called configuration design. The constraint satisfaction problem in configuration design may be difficult to solve due to the lack of tight constraints and the countless combinations of the layout; a diversity of solutions that satisfy the constraints should be allowed. Therefore, to allow such diversity, we directed our attention to developmental processes in biology and proposed an adaptive-growth-type 3D representation based on evolutionary algorithms. Here, the adaptive-growth-type means the shape expressed in the process, which develops through interaction with an outside environment, like shape generation of a living organism in the natural world. The usefulness of the representation was verified by applying it to the component layout problem in the early stage of satellite design.

A voxel-based shape representation when integrated with an evolutionary algorithm offers a number of potential advantages for shape optimization. Topology need not be predefined, geometric constraints are easily imposed and, with adequate resolution, any shape can be approximated to arbitrary accuracy. However, lack of boundary smoothness, length of chromosome, and inclusion of small holes in the final shape have been stated as problems with this representation. This paper describes two experiments performed in an attempt to address some of these problems. First, a design problem with only a small computational cost of evaluating candidate shapes was used as a testbed for designing genetic operators for this shape representation. Second, these operators were refined for a design problem using a more costly finite element evaluation. It was concluded that the voxel representation can, with careful design of genetic operators, be useful in shape optimization.