Most League Championship Algorithm (LCA). To date, several

Most of the previously developed watermarking
methods usually determine their parameters experimentally. Although, because
watermarking algorithms have large parameter space, it is usually difficult to
experimentally determine optimal watermarking parameters. A good solution for
this problem is to regard it as an optimization problem. Hence, metaheuristic
optimization techniques (also called advanced optimization techniques) have
emerged as a considerable tool in recent years. Considering the nature of the
phenomenon, Rao et al. 3 have divided the
population-based heuristic algorithms into two different groups: evolutionary
algorithms (EA) and swarm intelligence algorithms. Some of the recognized
evolutionary algorithms are: Genetic Algorithms (GA), Differential Evolution
(DE), Evolutionary Strategy (ES), Evolutionary Programming (EP), and Artificial
Immune Algorithm (AIA). Also some of the well-known swarm intelligence
algorithms are: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO),
Shuffled Frog Leaping (SFL) algorithm, and Artificial Bee Colony (ABC)
algorithm. In addition to these algorithms, there are some other algorithms
which work on the principles of different natural phenomena. Some of them are:
Harmony Search (HS) algorithm, Gravitational Search Algorithm (GSA),
Biogeography-Based Optimization (BBO) and League Championship Algorithm (LCA).
To date, several watermarking methods have been proposed by using the
metaheuristic optimization techniques. Shih and Wu 4 presented a watermarking scheme
based on DCT and genetic algorithm (GA) in which GA is applied to correct the
watermarking rounding errors. Wang et al. 5 presented a multi-objective
genetic algorithm (GA) based image watermarking method. They used a
multi-objective genetic algorithm with a variable-length mechanism to
automatically optimize the watermarking parameters and search the most suitable
positions for embedding watermark bits. Ebrahimi Moghaddam and Nemati 6 proposed a robust watermarking
technique using Imperialistic Competition Algorithm (ICA) in the spatial domain
where the ICA is used to find a suitable location for watermark embedding in
different color channels. In Agarwal et al. 7, a hybrid GA-BPN intelligent
network based watermarking scheme was proposed, in which the HVS
characteristics of four host images in DCT domain are used to obtain a sequence
of weighting factor from a GA-BPN. Then this weighting factor is used to embed
a binary watermark image in the host image in the DWT domain in LL3 sub band.
In Horng et al. 8, a blind image watermarking
method is introduced through a hybridization of DCT and SVD based on GA where
in the singular value of DCT-transformed host image is modified with the
quantizing value that is found using GA. Maity et al. 9 proposed a collusion resilient
optimized spread spectrum image watermarking scheme by using genetic algorithms
(GA) and multiband wavelets where the GA was employed to determine threshold
value of the host image coefficients (process gain e.g. the length of spreading
code) and the respective embedding strengths compatible to the gain of
frequency response. Also they proposed in paper 10 an multicarrier spread spectrum
image watermarking algorithm using hybridization of genetic algorithms (GA) and
neural networks (NN) where the GA selects appropriate gradient thresholds for
partitioning the host image and calculating the embedding strengths. The NN, as
well, calculates the weight factor in minimum mean square error combining
(MMSEC) to improve the watermark decoding performance and interference cancelation.
In Ali et al. 11, a watermarking scheme based on
differential evolution (DE) in discrete wavelet transform-singular value
decomposition (DWT–SVD) transform domain is proposed where the DE is used to
search optimal scaling factors for improving imperceptibility and robustness.
Peng et al. 12 introduces a ridgelet based
image watermarking algorithm, and then develops a novel watermarking framework
based on tissue P systems in which a special membrane structure is designed and
its cells are used as parallel computing units to find the optimal watermarking
parameters. In Abdelhakim et al. 13, a recent watermarking scheme is
utilized as the embedding algorithm and also the Artificial Bee Colony (ABC) is
selected as the optimization method in which the fitness function is used. The
fitness function is based on dividing the problem into two single objective
optimization sub-problems in which the robustness and imperceptibility
objectives are optimized separately. So, there is no need for weighting
factors. Ansari and Pant 14 proposed a multipurpose image
watermarking scheme in order to provide tampering detection and ownership
verification in which principal components of watermark is used to robust
watermark embedding and the last two LSB of host image is applied for the
fragile watermark embedding. The robust insertion is optimized with the help of
Artificial Bee colony (ABC) by optimization of 
the scaling factors. Ansari et al. 15, introduced a secure optimized
image watermarking ABC scheme in which the values of scaling factors are found
with the help of artificial bee colony (ABC). In paper 16, a semi blind image watermarking
scheme in wavelet domain based on Artificial Bee Colony (ABC) is proposed where
the encrypted watermark is embedded into wavelet coefficients by utilizing
reference image generated by using SVD and scaling factor. The ABC method is
employed to optimize the scaling factor. The main limitation of all the
mentioned algorithms is having algorithm-specific parameters that tuning these
parameters is important for finding the optimum solution and it is an
optimization problem itself. For example, the GA requires the crossover
probability, mutation probability and selection operator. So inappropriate
tuning of algorithm-specific parameters affects the effectiveness of the
algorithm and either increases the computational efforts or yields the local
optimum solution 3. Hence for solving this problem,
Rao et al. 17-19 presented a new
optimization algorithm known as “Teaching-Leaning-Based Optimization (TLBO)”
algorithm which requires only the common control parameters like population
size and number of generations for its working. For it does not require any
algorithm-specific parameter to be tuned, its implementation is simpler than
others algorithms.