Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic optimization problems (DOPs) have been a major challenge for ant colony optimization (ACO) algorithms. The integration of ACO algorithms with immigrants schemes showed promising results on different DOPs. Each type of immigrants scheme aims to address a DOP with specific characteristics. For example, random and elitism-based immigrants perform well on severely and slightly changing environments, respectively. In this paper, two hybrid immigrants, i.e., non-interactive and interactive, schemes are proposed to combine the merits of the aforementioned immigrants schemes. The experiments on a series of dynamic travelling salesman problems showed that the hybridization of immigrants further improves the performance of ACO algorithms

Ant Colony Optimization for the Electric Vehicle Routing Problem

Ant colony optimization (ACO) algorithms have proved to be powerful tools to solve difficult optimization problems. In this paper, ACO is applied to the electric vehicle routing problem (EVRP). New challenges arise with the consideration of electric vehicles instead of conventional vehicles because their energy level is affected by several uncertain factors. Therefore, a feasible route of an electric vehicle (EV) has to consider visit(s) to recharging station(s) during its daily operation (if needed). A look ahead strategy is incorporated into the proposed ACO for EVRP (ACO-EVRP) that estimates whether at any time EVs have within their range a recharging station. From the simulation results on several benchmark problems it is shown that the proposed ACO-EVRP approach is able to output feasible routes, in terms of energy, for a fleet of EVs

Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Rotation Peak Benchmark Generator (DRPBG) and Dynamic Composition Benchmark Generator (DCBG)

Based on our previous benchmark generator for the IEEE CEC’12 Competition on Dynamic Optimization, this report updates the two benchmark instances where two new features have 1been developed as well as a constraint to the benchmark instance of the dynamic rotation peak benchmark generator. The source code in C++ language for the two benchmark instances is included in the library of EAlib, which is an open platform to test and compare the performances of EAs

Pheromone modification strategy for the dynamic travelling salesman problem with weight changes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Ant colony optimization (ACO) algorithms have proved to be able to adapt in problems that change dynamically. One of the key issues for ACO when a change occurs is that the pheromone trails generated in the previous environment will not be compatible with the new environment. Therefore, the optimization process may be biased from the pheromone trails of the previous environment and fail to search for the newly generated global optimum. In this paper, we consider the dynamic travelling salesman problem (DTSP) in which the weights of the arcs are modified. A pheromone strategy that utilizes change-related information and regulates heuristically the pheromone trails of the affected arcs is proposed. From the experimental results the heuristic-based pheromone strategy performs statistically significant better in most DTSP test cases than other peer ACO algorithms

Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Travelling Salesman Problem Benchmark Generator

In this report, the dynamic benchmark generator for permutation-encoded problems for the travelling salesman problem (DBGPTSP) proposed in is used to convert any static travelling salesman problem benchmark to a dynamic optimization problem, by modifying the encoding of the instance instead of the fitness landscape

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance

An adaptive evolutionary algorithm for bi-level multi-objective VRPs with real-time traffic conditions

The file attached to this record is the author's final peer reviewed version.The research of vehicle routing problem (VRP) is significant for people traveling and logistics distribution. Recently, in order to alleviate global warming, the VRP based on electric vehicles has attracted much attention from researchers. In this paper, a bi-level routing problem model based on electric vehicles is presented, which can simulate the actual logistics distribution process. The classic backpropagation neural network is used to predict the road conditions for applying the method in real life. We also propose a local search algorithm based on a dynamic constrained multi-objective optimization framework. In this algorithm, 26 local search operators are designed and selected adaptively to optimize initial solutions. We also make a comparison between our algorithm and 3 modified algorithms. Experimental results indicate that our algorithm can attain an excellent solution that can satisfy the constraints of the VRP with real-time traffic conditions and be more competitive than the other 3 modified algorithms

Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic travelling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments

Route Optimization of Electric Vehicles based on Dynamic Wireless Charging

open access articleOneofthebarriersfortheadoptionofelectricvehicles(EVs)istheanxietyaroundthelimited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this paper, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called mobile energy disseminators (MEDs) which operate as mobile charging stations. We present a methodforroutingEVsaroundMEDsontheroadnetwork,whichisbasedonconstraintlogicprogramming and optimization using a graph-based shortest path algorithm. The proposed method exploits inter-vehicle communications in order to eco-route electric vehicles. We argue that combining modern communications betweenvehiclesandstateofthearttechnologiesonenergytransfer,thedrivingrangeofEVscanbeextended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs

An adaptive multi-population evolutionary algorithm for contamination source identification in water distribution systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Real-time monitoring of drinking water in a water distribution system (WDS) can effectively warn and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, non-uniqueness, and large scale characteristics. To address the real-time and non-uniqueness challenges, we propose an adaptive multi-population evolutionary optimization algorithm to determine the real-time characteristics of contamination sources, where each population aims to locate and track a different global optimum. The algorithm adaptively adjusts the number of populations using a feed-back learning mechanism. To effectively locate an optimal solution for a population, a co-evolutionary strategy is used to identify the location and the injection profile separately. Experimental results on three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms