478 research outputs found
Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm
based on the self-organizing behavior of individuals in a simulated social
environment. SOMA performs iterative computations on a population of potential
solutions in the given search space to obtain an optimal solution. In this
paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been
proposed that introduces a novel strategy to generate perturbations
effectively. This strategy allows the individual to span across more possible
solutions and thus, is able to produce better solutions. A comprehensive
analysis of OSOMA on multi-dimensional unconstrained benchmark test functions
is performed. OSOMA is then applied to solve real-time Dynamic Traveling
Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and
simulated using real-time data from Google Maps with a varying cost-metric
between any two cities. Although DTSP is a very common and intuitive model in
the real world, its presence in literature is still very limited. OSOMA
performs exceptionally well on the problems mentioned above. To substantiate
this claim, the performance of OSOMA is compared with SOMA, Differential
Evolution and Particle Swarm Optimization.Comment: 6 pages, published in CISS 201
QUORUM SENSING BASED BACTERIAL SWARM OPTIMIZATION ON TEST BENCHMARK FUNCTIONS
The Bacterial swarm optimization is one of the latest optimization technique mainly inspired from the swarm of bacteria. This paper introduces an intelligent Quorum sensing based Bacterial Swarm Optimization (QBSO) technique for testing and validation. The quorum sensing senses the best position of the bacteria by knowing the worst place in search space. By knowing these positions, the best optimal solution is attained. Here in this proposed QBSO algorithm the exploration capability of the bacteria is well improved. The proposed technique is validated on the seven standard benchmark with unimodal and multimodal test function for its feasibility and optimality. The basic swarm based optimization algorithms such as Particle Swarm Optimization, Ant Colony Optimization, Biogeography Based Optimization, Simulated Bee Colony and conventional Bacterial Swarm Optimization with the standard parameters are simulated and associated with the proposed technique. The attained results evidently indicate that the proposed method outperforms from the considered optimization methods. Further, the proposed technique may apply to any engineering problems, especially for complex real time optimization problems
Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines
This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems
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