Noise suppression for shape-gain vector quantization by index assignment using ant colony systems

Shieh, Chin‐Shiuh; Pan, Jeng‐Shyang; Su, Chen; Laio, B.Y.

doi:10.1109/icics.2003.1292450

Cited by 1 publication

(1 citation statement)

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“…Ho et al (2006) proposed an algorithm that incorporated key features of the tabu-search method in the development of a relatively simple but robust global ACO algorithm and used numerical results to validate and demonstrate the feasibility and effectiveness of the proposed algorithm in solving Electromagnetic (EM) design problems. Shieh et al (2003) focused on the transmission of codebook indices in a noisy environment, to minimize the impact of channel noise, using ACO to find a suitable index assignment and reported that the channel distortion was substantially reduced without incurring extra cost such as that in error-detection code and error-correction code. Eldos et al (2013a) used the ACO algorithm to solve the Printed Circuits Boards Drilling Problem (PCBDP), by finding the best order to drill each set of holes of the same diameter.…”

Section: Introductionmentioning

confidence: 99%

Maximally Distant Codes Allocation Using Chemical Reaction Optimization and Ant Colony Optimization Algorithms

Eldos¹,

Nazih²,

Kanan³

2015

Journal of Computer Science

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Error correcting codes, also known as error controlling codes, are set of codes with redundancy that allows detecting errors. This is quite useful in transmitting data over a noisy channel or when retrieving data from a storage with possible physical defects. The idea is to use a set of code words that are maximally distant from each other, hence reducing the chance of changing a valid codeword to another valid codeword by flipping bits. The problem can be viewed as picking m codes out of 2 n available nbit combinations, such that the aggregate hamming distance among those codewords is maximized. Due to the large solution spaces of such problems, greedy algorithms are sometimes used to generate quick and dirty solutions. However, modern evolutionary search algorithms like genetic algorithms, swarm particles, gravitational search and others, offer good alternatives, yielding near optimal solutions in exchange for some time. Chemical Reaction Optimization (CRO) has emerged as a new evolutionary algorithm to solve complex optimization problems. This algorithm mimics the molecular interactions towards finding a minimal energy state, which corresponds to an optimal solution for the problem in hand. In this research, we proposed a solution for the maximally distant codes allocation problem, through a binary knapsack mapping and compared the performance with the well established Ant Colony Optimization (ACO) algorithm, which is inspired by the ant's capability to find the shortest path between the nest and source of food. The binary knapsack mapping was used in the two algorithms. Test results showed that the CRO outperformed the ACO in every metric given any time budget.

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Section: Introductionmentioning

confidence: 99%