Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design

In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method

A Review on Maximum Power Point Tracking (MPPT) Technique for PV Power Generation System

The growing concern on environmental issues caused by fossil fuels and, indeed, on the availability of such energy resources in a long-run basis have settled the ground for the spreading of the so-called green energy sources. Among them, photovoltaic energy stands out due to the possibility of turning practically any household into a micro power plant. One important aspect about this source of energy is that practical photovoltaic generators are equipped with maximum power point tracking (MPPT) systems. Currently, researchers are focused on developing MPPT algorithms for partial shaded panels. In this paper review is presented on the emerging topologies for PV applications that could be used in the generation of new smart inverters

Role of Evolutionary Algorithms in Construction Projects Scheduling

Due to the increase in the stakeholders and their objectives the construction projects have significantly been affected by the ongoing demands leading to increase in complexity of scheduling problems, research in the field of Multi-Objective Optimization (MOO) have increased significantly. Through their population-based search methodologies, Evolutionary Algorithms drove attention to their efficiency in addressing scheduling problems involving two or three objectives. Genetic Algorithms (GA) particularly have been used in most of the construction optimization problems due to their ability to provide near-optimal Pareto solutions in a reasonable amount of time for almost all objectives. However, when optimizing more than three objectives, the efficiency of such algorithms degrades and trade-offs among conflicting objectives must be made to obtain an optimal Pareto Frontier. To address that, this paper aims to provide a comparative analysis on four evolutionary algorithms (Genetic algorithms – Memetic algorithms – Particle Swarm – Ant colony) in the field of construction scheduling optimization, gaps are addressed, and recommendations are proposed for future research development

Towards a Smart World: Hazard Levels for Monitoring of Autonomous Vehicles’ Swarms

This work explores the creation of quantifiable indices to monitor the safe operations and movement of families of autonomous vehicles (AV) in restricted highway-like environments. Specifically, this work will explore the creation of ad-hoc rules for monitoring lateral and longitudinal movement of multiple AVs based on behavior that mimics swarm and flock movement (or particle swarm motion). This exploratory work is sponsored by the Emerging Leader Seed grant program of the Mineta Transportation Institute and aims at investigating feasibility of adaptation of particle swarm motion to control families of autonomous vehicles. Specifically, it explores how particle swarm approaches can be augmented by setting safety thresholds and fail-safe mechanisms to avoid collisions in off-nominal situations. This concept leverages the integration of the notion of hazard and danger levels (i.e., measures of the “closeness” to a given accident scenario, typically used in robotics) with the concept of safety distance and separation/collision avoidance for ground vehicles. A draft of implementation of four hazard level functions indicates that safety thresholds can be set up to autonomously trigger lateral and longitudinal motion control based on three main rules respectively based on speed, heading, and braking distance to steer the vehicle and maintain separation/avoid collisions in families of autonomous vehicles. The concepts here presented can be used to set up a high-level framework for developing artificial intelligence algorithms that can serve as back-up to standard machine learning approaches for control and steering of autonomous vehicles. Although there are no constraints on the concept’s implementation, it is expected that this work would be most relevant for highly-automated Level 4 and Level 5 vehicles, capable of communicating with each other and in the presence of a monitoring ground control center for the operations of the swarm

Research on UBI auto insurance pricing model based on parameter adaptive SAPSO optimal fuzzy controller

Aiming at the problem of “dynamic” accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A five-dimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates

Research on UBI Auto Insurance Pricing Model Based on Parameter Adaptive SAPSO Optimal Fuzzy Controller

Aiming at the problem of “dynamic” accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A five-dimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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