The paper proposes a novel parallel optimization method to solve cooperative path-planning problem for a network of multiple unmanned aerial vehicles (UAVs) for disaster management. The path-planning problem addressed here is framed in the settings of an optimization problem involving eight objectives. The problem is solved by independent optimization of the objectives in parallel. The scope of parallel optimization enhances the efficiency to thoroughly explore individual multi-modal fitness landscapes. It in turn increases the robustness of the movements of multiple UAVs in complex terrain. Firefly Algorithm (FA) is selected as the base algorithm because of its established performance. Moreover, the decisive control parameters of FA are adapted based on their performance history with an aim to balance between the explorative and exploitative capabilities. Experimental results demonstrate the superiority of the proposed algorithm over its competitors.

The paper proposes a novel extension of many objective optimization using differential evolution (MaODE). MaODE solves a many objective optimization (MaOO) problem by parallel optimization of individual objectives. MaODE involves N populations, each created for an objective to be optimized using MaODE. The only mode of knowledge transfer among populations in MaODE is the modified version of mutation policy of DE, where every member of the population during mutation is influenced by the best members of all the populations under consideration. The present work aims at further increasing the communication between the members of the population by communicating between a superior and an inferior population, using a novel migration strategy. The proposed migration policy enables poor members of an inferior population to evolve with a superior population. Simultaneously, members from the superior population are also transferred to the inferior one to help it improving its performance. Experiments undertaken reveal that the proposed extended version of MaODE significantly outperforms its counterpart and the state-of-the-art techniques.

The paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution here is adapted based on the noisy fitness profile in the local surrounding of the given solution. The fitness estimate and the fitness variance of a sub-population surrounding the given solution are jointly used to signify the degree of noise contamination in its local neighborhood (LN). The adaptation of sample size based on the characteristics of the fitness landscape in the LN of a solution is realized here with the temporal difference Q-learning (TDQL). The merit of the present work lies in utilizing the reward-penalty based reinforcement learning mechanism of TDQL for sample size adaptation. This sidesteps the prerequisite setting of any specific functional form of relationship between the sample size requirement of a solution and the noisy fitness profile in its LN. Experiments undertaken reveal that the proposed algorithms, realized with artificial bee colony, significantly outperform the existing counterparts and the state-of-the-art algorithms.

This paper intends to develop a novel methodology for modeling a mind-controlled type-writer system to cater the needs of individuals suffering from various communication related disorders. This objective is fulfilled by first capturing EEG signals from ten subjects involved in mental utterance of seven vowel sounds. The eLORETA analysis of the acquired signals confirms the involvement of occipital, parietal and prefrontal lobes for this cognitive activity. The procured signals undergo the process of feature extraction after eradication of artifacts and are transferred to a novel capsule network module for categorization of seven class labels. Performance analysis undertaken confirms the superlative behavior of the proposed classifier with respect to other standard ones. Moreover, statistical evaluation also assures the superior performance of the proposed classifier model. A coding scheme has also been proposed to signify the consonants by the coalescence of two vowel sounds segregated by a space. Thus, the proposed methodology can be effectively utilized as a mind controlled type writing system to serve the needs of disabled individuals. Additionally, this technique can also be used in certain military scenarios that demand non-verbal communication as a secure option and in various BCI based gaming applications.

Protein complexes play a very important role in biological processes. The identification of the proteins in the protein complex as well as the prediction of protein complexes which are hitherto anonymous will improve the understanding of the protein complexes and related biological processes. Most of the computational methods used to predict protein complexes were based on the principle that proteins inside the complex have more interactions. The protein-protein interaction (PPI) network is thus used to predict protein complexes. The features that affect the interaction of proteins in the protein-protein interaction network are mostly studied for identification of protein complexes. This paper is used to analyze the effect of the characteristic feature related to domains and functions on protein complex prediction. In this paper a ranking approach is applied to the solutions of differential evolution to identify protein complexes. The experimental results show that the proposed method beats the existing methods taking into consideration the typical performance metrics.

The paper proposes two novel extensions of the learning automata induced noisy bee colony (LANBC) to handle the presence of stochastic noise in the fitness landscape. The reward/penalty based reinforcement learning scheme of the stochastic learning automata (SLA) of LANBC helps a solution to prudently select a sample size for its periodic fitness evaluations based on the fitness variance in its local neighborhood. However, the LANBC suffers from two stalemates, including weak search dynamic in the presence of noise and the deterministic parental selection scheme leading to dismissal of quality solutions and loss of population diversity. The paper overcomes these impasses by employing two novel strategies. First, the search dynamic is amended with an aim to generate promising offspring solutions by appropriately tuning the control parameter based on the contamination effect of noise on other population members. Second, a modified probabilistic crowding induced niching behavior is introduced to promote quality solutions to the next generation to ensure both the population quality and the population diversity. Computer simulations undertaken on the noisy versions of a set of 28 benchmark functions reveal that the proposed algorithm outperforms its contenders with respect to function error value.

The proposed system aims at automatic identification of an unknown dance posture referring to the 34 primitive postures of ballet, simultaneously measuring the proximity of an unknown dance posture to a known primitive. A simple and novel seven stage algorithm achieves the desired objective. Skin color segmentation is performed on the dance postures, the output of which is dilated and edge is detected. From the boundaries of the postures, connected components are identified and the boundary is piecewise linearly approximated using modified artificial bee colony algorithm. Here, lies the novelty of our work. From the approximated boundary, features are extracted in terms of internal angles. This whole procedure is repeated for all the training images as well as testing image. The classification of the training image containing ballet posture is done using Euclidean distance matching.

The paper aims at classifying the learning-skill of driving-learners by utilizing the acquired P300 and N400 event related potentials during the learning phase of a simulated car-driving task. A set of driving stimuli, including braking on sudden appearance of bumpers, steering control at the sharp bending points, and the like, are prepared to experimentally classify the learning skill of the subjects in three distinct classes: Low, Medium and High. The complexity in classifying the subjective learning-skill arises because of intra- and inter-session variations in brain signals due to temporal fluctuation in activation levels of the involved brain lobes. A novel z-slice based model of general type-2 fuzzy set is proposed to represent the intra- and inter-session variations by the footprint of uncertainty and their composite variations by z-slices at different values of a given feature. Further, a novel technique for z-slice based classifier is proposed to classify the learning-skill under intra-and inter-session uncertainty. The proposed classifier is found to outperform traditional and well-known type-2 classifiers for the present learning-skill classification task in Experiments undertaken confirm the frontal and parietal theta (4-7 Hz) activation in discrete learning steps. The proposed research outcome may directly be utilized to certify driving learners with required learning skills.

We study the vowels detection from brain activation due to vowel sound imageries. At first we experimentally determine the maximum and relatively longer activation that takes place in the frontal or pre frontal lobe during vowel sound Imagination using acquired electroencephalographic signal analysis. Then we capture pre-frontal or frontal vowel sound imagery using a functional near infrared device to extract certain statistical features. Differential evolution based feature selection is used to for dimensionality reduction. The reduced feature set is then used to design an interval type 2 fuzzy classifier to classify the vowels from the pre frontal or frontal f-NIRs response to vowel sound imagination. Experiments undertaken confirm that the proposed classifier outperforms its competitors in classification accuracy for each vowel sound imagery class. They further confirm that the f-NIRs based classification outperforms EEG based modality for better capture of brain activations. Consonants are encoded with two vowel sounds with a space between them. Thus the proposed technique can effectively be used for mind driven type writing of vowels and consonants, serving people suffering from vocal deficiency.

We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranking policy, instead of the Pareto ranking, supposing that a solution is unlikely to perform well for all objectives in a MaOO problem. A solution is thus evolved with respect to a specific objective only, which it may proficiently optimize. First, all objectives of the MaOO problem are individually optimized by evolutionary algorithms in parallel. The second step is concerned with judiciously selecting and filtering the quality solutions obtained by individual optimization of all objectives in parallel. A unique ranking policy is proposed to grade the members of the union set of quality solutions based on their extent of optimization of individual objectives. The evolutionary algorithm used for parallel optimization of all objectives in a MaOO here has been realized with differential evolution (DE). The mutation strategy of DE is also amended here with an aim to allow controlled communication between population members, concerned with parallel optimization of different objectives of a MaOO problem. Experiments undertaken with DTLZ and WFG test suits reveal that the proposed algorithm outperforms the state-of-art techniques with respect to inverted generational distance and hypervolume metrics.

We propose two extensions of the traditional artificial bee colony algorithm to proficiently optimize noisy fitness. The first strategy is referred to as stochastic learning automata induced adaptive sampling. It is employed with an aim to judiciously select the sample size for the periodic fitness evaluation of a trial solution, based on the fitness variance in its local neighborhood. The local neighborhood fitness variance is here used to capture the noise distribution in the local surrounding of a candidate solution of the noisy optimization problem. The second strategy is concerned with determining the effective fitness estimate of a trial solution using the distribution of its noisy fitness samples, instead of direct averaging of the samples. Computer simulations undertaken on the noisy versions of a set of 28 benchmark functions reveal that the proposed algorithm outperforms its contenders with respect to function error value in a statistically significant manner.

The paper attempts to model human working memory using fuzzy relational equation with an aim to retrieve the relevant stored information in the memory from the partial input using the model. Psycho-physiological experiments have been developed to validate the model to match the model generated memory-response with the actual memory response using EEG signals acquired during memory encoding and recall phases. The fuzzy relational equation developed here represents brain connectivity in the fuzzy space between the encoding and the recall instances. The paper introduces a novel approach to compute inverse fuzzy relation with respect to max-min composition operator to determine the short-term memory information from the working memory, when the latter is stimulated with partial faces of people already encoded in the short-term memory. An error metric is defined to measure the error amplitude between the model-predicted encoding pattern and the actual pattern encoded in the short-term memory. A small value in error indicates a good accuracy of the proposed working memory model, and thus can be used to discriminate people with memory failure. Experiments undertaken reveal that the error metric could be used successfully to detect memory failures in five patients, two of which suffer from Parkinson, two from the early Alzheimer's disease and one from frontal lobe damage.

This paper formulates the protein-protein interaction (PPI) prediction problem as a multi-objective optimization (MOO) problem. The focus here is to jointly maximize i) the number of common neighbors of the proteins predicted to be interacting, ii) their functional similarity, and iii) the ratio between their individual accessible solvent area and that of the corresponding protein-protein complex. The above MOO problem is solved using a fusion of the differential evolution for multi-objective optimization and the stochastic learning automata. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods with respect to sensitivity, specificity, and F1 score.

This paper proposes a novel evolutionary approach to the optimal selection of electrodes as well as relevant EEG features for effective classification of cognitive tasks. The problem has been formulated in the framework of a single objective optimization problem with an aim to simultaneously satisfy three criteria. The first criterion deals with maximization of the correlation between the features of EEG sources before and after the selection of the optimal electrodes. The second criterion is concerned with minimization of the mutual information between the features of the selected EEG electrodes. The last criterion aims at maximization of the ratio of the difference between the selected features of the EEG sources between and within any two cognitive tasks. A self-adaptive variant of firefly algorithm is proposed to solve the above optimization problem by proficiently balancing the trade-off between the computational accuracy and the run-time complexity. Experiments undertaken over wide variety of cognitive tasks reveal that the proposed algorithm outperforms the other standard algorithms (applied to the same problem) in terms of accuracy and computational overhead.

The paper aims at solving a multi-robot box-pushing problem in the presence of noisy sensory data using evolutionary algorithm. In the box-pushing problem by twin robots, the range data obtained by the robots at any instant of time are measurements, and the torque and/or force to be developed by the robots for a local movement of the box are estimators. We here use torques and forces developed by the robots to construct two objectives on minimization of the total energy consumed and the total time required for a local movement of the box in the presence of noisy sensory data. The box-pushing problem is solved using the proposed extended version of the noisy non-dominated sorting bee colony (ENNSBC) algorithm to handle noise in the objective surfaces. Experiments undertaken in both simulation and real-world platforms reveal the superiority of the proposed ENNSBC to other competitor algorithms to solve the box-pushing problem with respect to the performance metrics defined in the literature.

In this paper, a novel multi-stage approach to knowledge extraction from a time-series is proposed. A given time-series is modeled as a sequence of well-known primitive patterns with the purpose of identifying first-order probabilistic transition rules for prediction. The first stage of the proposed model segments a time-series into structurally distinct temporal blocks of non-uniform length such that each block possesses a relatively low variation of dynamic slope. In the second stage, the temporal segments thus obtained are normalized and matched with one of four well-known primitive patterns using a fuzzy matching algorithm. Finally, the sequence of matched segments is used to represent the time-series as a set of four directed graphs corresponding to the four primitive patterns. Each vertex in the graphs represents a horizontal partition of the time-series and each directed edge indicates the transitions between two such partitions caused by the occurrence of one or more temporal segments. In the test phase, the graphs are employed to predict possible future values of the time-series. Experiments carried out on the TAIEX close-price time-series indicate a high prediction accuracy, thereby validating the use of the model for real-life forecasting applications.

Protein-Protein Interactions (PPIs) play an important role in various cellular processes. This paper attempts to solve the PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous minimization of functional dissimilarity, intra- as well as inter-molecular energy and the difference in phylogenetic profiles of interacting proteins. The above optimization problem is solved using Firefly Algorithm with Non-dominated Sorting. The proposed technique outperforms existing methods, including gene-ontology based Relative Specific Similarity, Fuzzy SVM, phylogenetic profile and evolutionary/swarm algorithm based approaches, with respect to sensitivity, specificity and F1 score.

A novel multi-objective optimization algorithm is introduced in the paper to proficiently obtain Pareto-optimal solutions in the noisy fitness landscapes. First, a non-linear functional relationship between the fitness variance in the local neighborhood of a trial solution and the sample size for its periodic fitness evaluation is proposed. The second strategy is concerned with determining defuzzified centroidal value of the noisy fitness samples, instead of their conventional averaging, as the effective fitness measure of the trial solutions. Finally, to ensure the diversity of quality solutions in the noisy fitness landscapes, a new selection criterion induced by the crowding distance measure and the distribution pattern of noisy fitness samples is formulated. Experiments undertaken to validate the performance of the extended algorithm affirm its superiority to its contenders with respect to hyper volume ratio, when examined on a test suite of 23 standard benchmarks contaminated with additive noise of five statistical distributions.

This paper provides a novel approach to predict the Protein-Protein Interaction (PPI) network using a modified version of the Bat Algorithm. The attractive trait of the proposed approach is that it attempts to analyze the impact of physicochemical properties, structural features and evolutionary relationship of proteins, to predict the PPI network. Computer simulations reveal that our proposed method effectively predicts the PPI of Saccharomyces Cerevisiae with a sensitivity of (0.85) and specificity of (0.87) and outperforms other state-of-art methodologies.

The present work introduces a proposed Artificial Bee Colony with Adaptive Scale Factor (ABC-ASF) optimization algorithm-based optimal electrode selection strategy from which the acquired EEG signals enlighten the major brain activities involved in a cognitive task. In ABC-ASF, the scale factor for mutation in traditional Artificial Bee Colony is self adapted by learning from the previous experiences. Experimental results obtained from the real framework of estimating optimal electrodes indicate that the proposed algorithm outperforms other state-of-art techniques with respect to computational accuracy and run-time complexity.

The paper aims at designing new strategies to extend traditional Non-dominated Sorting Bee Colony algorithm to proficiently obtain Pareto-optimal solutions in presence of noise on the fitness landscapes. The first strategy, referred to as adaptive selection of sample-size, is employed to balance the trade-off between accurate fitness estimate and computational complexity. The second strategy is concerned with determining statistical expectation, instead of conventional averaging of fitness-samples as the measure of fitness of the trial solutions. The third strategy attempts to extend Goldberg's approach to examine possible placement of a slightly inferior solution in the optimal Pareto front using a more statistically viable comparator. Experiments undertaken to study the performance of the extended algorithm reveal that the extended algorithm outperforms its competitors with respect to four performance metrics, when examined on a test-suite of 23 standard benchmarks with additive noise of three statistical distributions.

This paper aims to design new strategies to extend traditional multiobjective optimization algorithms to efficiently obtain Pareto-optimal solutions in presence of noise on the objective surfaces. The first strategy, referred to as adaptive selection of sample size, is employed to balance the tradeoff between quality measure of fitness and run-time complexity. The second strategy is concerned with determining statistical expectation, instead of conventional averaging, of fitness samples as the measure of fitness of the trial solutions. The third strategy attempts to extend Goldberg's method to compare slightly worse trial solutions with its competitor by a more statistically viable comparator to examine possible placement of the former solution in the Pareto optimal front. The traditional differential evolution for multiobjective optimization algorithm has been modified by extending its selection step with the proposed strategies. Experiments undertaken to study the performance of the extended algorithm reveal that the extended algorithm outperforms its competitors with respect to three performance metrics, when examined on a test suite of 23 standard benchmarks with additive noise of three statistical distributions. The extended algorithm has been applied on the well known box-pushing problem, where the forces and torques required to shift the box by two robots are evaluated to jointly satisfy the conflicting objectives on task-execution time and energy consumption in presence of noise on range estimates from the sidewalls of the workspace. The application justifies the importance of the proposed noise-handling strategies in practical systems.

The paper addresses a novel approach to protein-ligand docking problem using Non-dominated Sorting Bee Colony optimization algorithm. In this work, protein- ligand docking is formulated as a multi-objective optimization problem. The docking energy, molecular weight and oral bioavailability are used as three scoring functions for the solutions. Results are demonstrated for six different target proteins both numerically and pictorially. Experimental results reveal that the proposed method outperforms Multi-Objective Particle Swarm Optimization, Non-dominated Sorting Genetic Algorithm-II and Artificial Bee Colony based ligand design method considering the three objectives of the evolved molecules.

An adaptive memetic algorithm incorporates an adaptive selection of memes (units of cultural transmission) from a meme-pool to improve the cultural characteristics of the individual member of a population-based search algorithm. The paper proposes an extension of Multi-objective Optimization realized with Differential Evolution algorithm by utilizing the composite benefits of Differential Evolution for Multi-objective Optimization (DEMO) for global search and Templocal refinementoral Difference Q-Learning (TDQL) for local refinement. Computer simulations performed on a well known set of 23 benchmark functions reveal that the proposed algorithm outperforms its competitors with respect to inverted generational distance, spacing and error ratio.

This paper provides a novel approach to design an Adaptive Firefly Algorithm using self-adaptation of the algorithm control parameter values by learning from their previous experiences in generating quality solutions. Computer simulations undertaken on a well-known set of 25 benchmark functions reveals that incorporation of Q-learning in Firefly Algorithm makes the corresponding algorithm more efficient in both runtime and accuracy. The performance of the proposed adaptive firefly algorithm has been studied on an automatic motion planing problem of nonholonomic car-like system. Experimental results obtained indicate that the proposed algorithm based parking scheme outperforms classical Firefly Algorithm and Particle Swarm Optimization with respect to two standard metrics defined in the literature.

In this paper, a novel design method of Generalized Type-2 Fuzzy Set (GT2FS) is proposed. The GT2FS involves the secondary memberships for individual primary membership curve, which is obtained here by formulating and solving an optimization problem. To accomplish this, we have used Differential Evolution. The secondary membership of a given source, symbolizing the consistency in its primary membership assignment, is determined with respect to the composite of all the users' primary membership functions. The performance of the proposed method of secondary membership evaluation has also been studied on an emotion recognition problem.

The paper proposes a novel approach to recognize smell stimuli from the electroencephalogram (EEG) signals acquired during the period of inhalation. The main contribution of the paper lies in feature selection by an evolutionary algorithm and pattern classification by Differential Evolution induced Hopfield neural network. One additional merit of the work lies in data point reduction by Principal component analysis. Experiments undertaken on 25 subjects with 10 smell stimuli indicate that the proposed scheme of feature selection, data point reduction and classification outperforms the traditional approach by a wide margin. Experimental results confirm that the smell stimuli excites the pre frontal lobe of the human brain and is responsible for a special type of brain rhythms (EEG signal) in alpha-band, theta-band and delta-band.

Memetic algorithms (MAs) are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolutions. An adaptive MA (AMA) incorporates an adaptive selection of memes (units of cultural transmission) from a meme pool to improve the cultural characteristics of the individual member of a population-based search algorithm. This paper presents a novel approach to design an AMA by utilizing the composite benefits of differential evolution (DE) for global search and Q-learning for local refinement. Four variants of DE, including the currently best self-adaptive DE algorithm, have been used here to study the relative performance of the proposed AMA with respect to runtime, cost function evaluation, and accuracy (offset in cost function from the theoretical optimum after termination of the algorithm). Computer simulations performed on a well-known set of 25 benchmark functions reveal that incorporation of Q-learning in one popular and one outstanding variants of DE makes the corresponding algorithm more efficient in both runtime and accuracy. The performance of the proposed AMA has been studied on a real-time multirobot path-planning problem. Experimental results obtained for both simulation and real frameworks indicate that the proposed algorithm-based path-planning scheme outperforms the real-coded genetic algorithm, particle swarm optimization, and DE, particularly its currently best version with respect two standard metrics defined in the literature.

Memetic algorithms are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolution. In this paper a synergism of the classical Differential Evolution algorithm and Q-learning is used to construct the memetic algorithm. Computer simulation with standard benchmark functions reveals that the proposed memetic algorithm outperforms three distinct Differential Evolution algorithms.

This paper provides a new approach to include the chemotactic behavior of Bacterial Foraging Algorithm (BFOA) in the existing Improved Harmony Search (IHS) algorithm. Extensive computer simulations with CEC-2005 benchmark functions reveal that the proposed algorithm outperforms the existing one with respect to accuracy in determining the optima. The proposed algorithm has successfully been implemented for multi-robot motion planning application. Performance has been studied using the proposed IHS-BFO algorithm and compared with existing IHS and Particle Swarm Optimization (PSO) algorithm.

The essence of the paper is to reduce uncertainty in interval type-2 fuzzy sets, and demonstrate the merit of uncertainty reduction in pattern classification problem. The area under the footprint of uncertainty has been used as the measure of uncertainty. A mathematical approach to reduce the area under the footprint of uncertainty has been proposed. Experiments have been designed to compare the relative performance of the classical interval type-2 fuzzy sets with its revised counterpart in emotion recognition from facial expression. Statistical tests performed favor the proposed results of uncertainty reduction. The proposed uncertainty reduction scheme helps in saving approximately 6% gain in classification accuracy with respect to one published work when applied to emotion recognition problem.