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Prototype optimization of reconfigurable mobile robots based on a modified Harmony Search method
Abstract
In this paper, the caster-and-camber-based reconfiguration of the trafficability metrics of mobile robots is first discussed. The trafficability metrics of the reconfigurable mobile robots, such as the equivalent lateral and longitudinal stability margins with non-linear characteristics, can be built up using the geometrical projection approach. We next propose a modified Harmony Search method to deal with the constrained optimization problems, which is based on the direct handling of the given constraints. It is further applied to obtain the optimal configurable prototypes of a mobile robot.
... In addition to the aforementioned applications, the HS has also been widely employed in a large variety of fields, including transportation, manufacturing, robotics, control, and medical science [24]. Xu et al. explore the applications of the HS in the prototype optimization and selection of the reconfigurable mobile robots in the sandy terrain [35,36]. Many traffic modeling software packages are capable of finding the optimal or near-optimal signal timings using different optimization algorithms. ...
The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem.
This paper presents a Negative Selection Algorithm (NSA)-based fault detection system for the mobile robots. The new scheme can take advantage of the healthy feature signals of the mobile robots so as to generate NSA detectors. Our method has been investigated using the testbed of a broken wheel fault detection case.
Many optimization problems in various fields have been solved using diverse optimization al gorithms. Traditional optimization techniques such as linear programming (LP), non-linear programming (NLP), and dynamic program ming (DP) have had major roles in solving these problems. However, their drawbacks generate demand for other types of algorithms, such as heuristic optimization approaches (simulated annealing, tabu search, and evolutionary algo rithms). However, there are still some possibili ties of devising new heuristic algorithms based on analogies with natural or artificial phenom ena. A new heuristic algorithm, mimicking the improvisation of music players, has been devel oped and named Harmony Search (HS). The performance of the algorithm is illustrated with a traveling salesman problem (TSP), a specific academic optimization problem, and a least-cost pipe network design problem.
This paper describes a newly developed rover with small size, light-weight, low power consumption. In recent years, many researchers have extensively studied and developed unmanned mobile robots for surface exploration of the moon or planets. A lunar or planetary rover is required to travel safely over a long distance for many days in unfamiliar terrain. This paper presents scientific signification, requirements, and technology of a lunar or planetary rover. This paper proposes a new mobility system, which has four wheels and one supported wheel. This novel suspension system is a simple and light mechanism like a four-wheeled rover and provides a high degree of mobility like a six-wheeled rover. The performance of the developed rover is shown by some experiments.
Most structural optimization methods are based on mathematical algorithms that require substantial gradient information. The selection of the starting values is also important to ensure that the algorithm converges to the global optimum. This paper describes a new structural optimization method based on the harmony search (HS) meta-heuristic algorithm, which was conceptualized using the musical process of searching for a perfect state of harmony. The HS algorithm does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. Various truss examples with fixed geometries are presented to demonstrate the effectiveness and robustness of the new method. The results indicate that the new technique is a powerful search and optimization method for solving structural engineering problems compared to conventional mathematical methods or genetic algorithm-based approaches.
This paper develops an Improved harmony search (IHS) algorithm for solving optimization problems. IHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented. The IHS algorithm has been successfully applied to various benchmarking and standard engineering optimization problems. Numerical results reveal that the proposed algorithm can find better solutions when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems.
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniques, it does not require niching (or any other similar approach) to maintain diversity in the population. We validated the algorithm using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach is a viable alternative to the traditional penalty function, mainly in engineering optimization problems.
Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using genetic algorithms (GAs) or classical optimization methods, penalty function methods have been the most popular approach, because of their simplicity and ease of implementation. However, since the penalty function approach is generic and applicable to any type of constraint (linear or nonlinear), their performance is not always satisfactory. Thus, researchers have developed sophisticated penalty functions specific to the problem at hand and the search algorithm used for optimization. However, the most difficult aspect of the penalty function approach is to find appropriate penalty parameters needed to guide the search towards the constrained optimum. In this paper, GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter. Careful comparisons among feasible and infeasible solutions are made so as to provide a search direction towards the feasible region. Once sufficient feasible solutions are found, a niching method (along with a controlled mutation operator) is used to maintain diversity among feasible solutions. This allows a real-parameter GA's crossover operator to continuously find better feasible solutions, gradually leading the search near the true optimum solution. GAs with this constraint handling approach have been tested on nine problems commonly used in the literature, including an engineering design problem. In all cases, the proposed approach has been able to repeatedly find solutions closer to the true optimum solution than that reported earlier.
This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm (GA) for numerical optimization. The proposed approach produces solutions even better than those previously reported in the literature for other (GA-based and mathematical programming) techniques that have been particularly fine-tuned using a normally lengthy trial and error process to solve a certain problem or set of problems. The present technique is also easy to implement and suitable for parallelization, which is a necessary further step to improve its current performance.
Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search (HS) meta-heuristic algorithm-based approach for engineering optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Various engineering optimization problems, including mathematical function minimization and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms.
Calculus has widespread applications in science and engineering. Optimization is one of its major subjects, where a problem can be mathematically formulated and its optimal solution is determined by using derivatives. However, this calculus-based derivative technique can only be applied to real-valued or continuous-valued functions rather than discrete-valued functions while there are many situations where design variables contain not continuous values but discrete values by nature. In order to consider these realistic design situations, this study proposes a novel derivative for discrete design variables based on a harmony search algorithm. Detailed analysis shows how this new stochastic derivative works in the bench-mark function and fluid-transport network design. Hopefully this new derivative, as a fundamental technology, will be utilized in various science and engineering problems.
This paper presents a simple mathematical analysis of some features of the Harmony Search algorithm (HS). HS is a recently developed derivative-free optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work analyses the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. Experimental results have been provided to validate the theoretical treatment. A simple modification of the classical HS has been proposed in the light of the mathematical analysis undertaken here.
Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.
- Geem Z.W.