Coverage Path Planning Using Mobile Robot Team Formations

The multi-robot coverage path-planning problem involves finding collision-free paths for a set of robots so that they can completely cover the surface of an environment. This problem is non-trivial as the geometry and location of obstacles in the environment is usually not known a priori by the robots, and they have to adapt their coverage path as they discover obstacles while moving in the environment. Additionally, the robots have to avoid repeated coverage of the same region by each other to reduce the coverage time and energy expended. This chapter discusses the research results in developing multi-robot coverage path planning techniques using mini-robots that are coordinated to move in formation. The authors present theoretical and experimental results of the proposed approach using e-puck mini-robots. Finally, they discuss some preliminary results to lay the foundation of future research for improved coverage path planning using coalition game-based, structured, robot team reconfiguration techniques.

Artificial Insect Algorithms for Routing in Wireless Sensor Systems

Social insect communities are formed from simple, autonomous, and cooperative organisms that are interdependent for their survival. These communities are able to effectively coordinate themselves to achieve global objectives despite a lack of centralized planning. This chapter presents a study of artificial insect algorithms for routing in wireless sensor networks, with a specific focus on simulating termites and their behaviours in their colony. The simulating behaviour demonstrates how the termites make use of an autocatalytic behaviour in order to collectively find a solution for a posed problem in reasonable time. The derived algorithm termed Termite-Hill demonstrates the principle of the termite behavior for solving the routing problem in wireless sensor networks. The performance of the algorithm was tested on static and dynamic sink scenarios. The results were compared with other routing algorithms with varying network density and showed that the proposed algorithm is scalable and improved on network energy consumption with a control over best-effort service.

Optimization of Drilling Process via Weightless Swarm Algorithm

In this chapter, the main objective of maximizing the Material Reduction Rate (MRR) in the drilling process is carried out. The model describing the drilling process is adopted from the authors' previous work. With the model in hand, a novel algorithm known as Weightless Swarm Algorithm is employed to solve the maximization of MRR due to some constraints. Results show that WSA can find solutions effectively. Constraints are handled effectively, and no violations occur; results obtained are feasible and valid. Results are then compared to previous results by Particle Swarm Optimization (PSO) algorithm. From this comparison, it is quite impossible to conclude which algorithm has a better performance. However, in general, WSA is more stable compared to PSO, from lower standard deviations in most of the cases tested. In addition, the simplicity of WSA offers abundant advantages as the presence of a sole parameter enables easy parameter tuning and thereby enables this algorithm to perform to its fullest.

A Complementary Cyber Swarm Algorithm

A recent study (Yin, et al., 2010) showed that combining Particle Swarm Optimization (PSO) with the strategies of Scatter Search (SS) and Path Relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. In this chapter, the authors propose a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the Tabu Search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long-term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to the Standard PSO 2007 method (Clerc, 2008). These findings motivate future investigations of Cyber Swarm methods that combine features of both the original and complementary variants and incorporate additional strategic notions from the SS/PR template as a basis for creating a still more effective form of swarm optimization.

Hybrid Swarm Intelligence-Based Biclustering Approach for Recommendation of Web Pages

This chapter focuses on recommender systems based on the coherent user's browsing patterns. Biclustering approach is used to discover the aggregate usage profiles from the preprocessed Web data. A combination of Discrete Artificial Bees Colony Optimization and Simulated Annealing technique is used for optimizing the aggregate usage profiles from the preprocessed clickstream data. Web page recommendation process is structured in to two components performed online and offline with respect to Web server activity. Offline component builds the usage profiles or usage models by analyzing historical data, such as server access log file or Web logs from the server using hybrid biclustering approach. Recommendation process is the online component. Current user's session is used in the online component for capturing the user's interest so as to recommend pages to the user for next navigation. The experiment was conducted on the benchmark clickstream data (i.e. MSNBC dataset and MSWEB dataset from UCI repository). The results signify the improved prediction accuracy of recommendations using biclustering approach.

A Particle Swarm Optimizer for Constrained Multiobjective Optimization

Generally, constraint-handling techniques are designed for evolutionary algorithms to solve Constrained Multiobjective Optimization Problems (CMOPs). Most Multiojective Particle Swarm Optimization (MOPSO) designs adopt these existing constraint-handling techniques to deal with CMOPs. In this chapter, the authors present a constrained MOPSO in which the information related to particles' infeasibility and feasibility status is utilized effectively to guide the particles to search for feasible solutions and to improve the quality of the optimal solution found. The updating of personal best archive is based on the particles' Pareto ranks and their constraint violations. The infeasible global best archive is adopted to store infeasible nondominated solutions. The acceleration constants are adjusted depending on the personal bests' and selected global bests' infeasibility and feasibility statuses. The personal bests' feasibility statuses are integrated to estimate the mutation rate in the mutation procedure. The simulation results indicate that the proposed constrained MOPSO is highly competitive in solving selected benchmark problems.

An Optimization Algorithm Based on Brainstorming Process

In this chapter, the human brainstorming process is modeled, based on which two versions of a Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc, which points at one direction for future research on BSO algorithms.

Image Segmentation Based on Bio-Inspired Optimization Algorithms

This chapter addresses the issue of image segmentation by clustering in the domain of image processing. Fuzzy C-Means is a widely adopted clustering algorithm. Bio-inspired optimization algorithms are optimal methods inspired by the principles or behaviors of biology. For the purpose of reinforcing the global search capability of FCM, five Bio-Inspired Optimization Algorithms (BIOA) including Biogeography-Based Optimization (BBO), Artificial Fish School Algorithm (AFSA), Artificial Bees Colony (ABC), Particle Swarm Optimization (PSO), and Bacterial Foraging Algorithm (BFA) are used to optimize the objective criterion function, which is interrelated to centroids in FCM. The optimized FCMs by the five algorithms are used for image segmentation, respectively. They have different effects on the results.

Path Relinking Scheme for the Max-Cut Problem

In this chapter, a path relinking method for the maximum cut problem is investigated. The authors consider an implementation of the path-relinking, where it is utilized as a subroutine for another meta-heuristic search procedure. Particularly, the authors focus on the global equilibrium search method to provide a set of high quality solutions, the set that is used within the path relinking method. The computational experiment on a set of standard benchmark problems is provided to study the proposed approach. The authors show that when the size of the solution set that is passed to the path relinking procedure is too large, the resulting running times follow the restart distribution, which guarantees that an underlying algorithm can be accelerated by removing all of the accumulated data (set P) and re-initiating its execution after a certain number of elite solutions is obtained.

Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization from a Population Diversity Perspective

Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm's ability of exploration and exploitation. Through the population diversity measurement, useful search information can be obtained. PSO with a different topology structure and a different boundary constraints handling strategy will have a different impact on particles' exploration and exploitation ability. In this chapter, the phenomenon of particles getting “stuck in” the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques, and analyze the impact of these settings on the algorithm's abilities of exploration and exploitation. From these experimental studies, an algorithm's abilities of exploration and exploitation can be observed and the search information obtained; therefore, more effective algorithms can be designed to solve problems.