Note: Intro -- Preface -- Contents -- A Comprehensive Review on Bacteria Foraging Optimization Technique -- 1 Introduction -- 2 Behavior of Bacterial Colony -- 2.1 Phases in the Life Cycle of Bacteria -- 2.2 Communication Among Bacteria -- 2.3 Types of Bacteria -- 3 E.coli Bacterial Colonies -- 3.1 Biological Inspiration -- 4 Description of BFO -- 4.1 Chemotaxis -- 4.2 Swarming -- 4.3 Reproduction -- 4.4 Elimination-Dispersal -- 4.5 Convergence -- 5 Optimization Based on E.coli Bacterial Colony -- 6 Classification of BFO Algorithm -- 6.1 Conventional BFO -- 6.2 Revised BFO -- 6.3 Hybrid BFO -- 7 Multi-objective Optimization Based on BF -- 7.1 Multi-objective Optimization -- 7.2 Multi-objective BFO Algorithm -- 8 An Overview of BFO Applications -- 9 Conclusion -- References -- Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis -- 1 Introduction -- 1.1 Outline of Swarm Intelligence -- 1.2 Multi-objective versus Many-objective Optimization -- 2 Swarm Intelligence for Multi Objective Problems -- 2.1 ACO for Multiple Objective Problems -- 2.2 PSO for Multiple Objective Problem -- 2.3 ABC for Multiple Objective Problem -- 3 Swarm Intelligence for Many Objective Optimization -- 4 Study of Swarm Intelligence for EEG Signal -- 4.1 ACO in EEG Signal Analysis -- 4.2 PSO in EEG Signal Analysis -- 4.3 ABC in EEG Signal Analysis -- 4.4 Towards Multiple and Many Objectives of EEG Signal -- 5 Discussion and Future Research -- References -- Comparison of Various Approaches in Multi-objective Particle Swarm Optimization (MOPSO): Empirical Study -- 1 Introduction -- 2 General Multi-objective Problem -- 3 Particle Swarm Optimization -- 4 PSO for Multiple Objectives -- 5 Approaches of MOPSO -- 5.1 Algorithms That Exploit Each Objective Function Separately -- 5.2 Objective Function Aggregation Approaches. , 5.3 Non-Pareto, Vector Evaluated Approaches -- 5.4 Pareto Dominance Approach -- 5.5 Weighted Sum Approach -- 5.6 Lexicographic Approach -- 5.7 Subpopulation Approach -- 5.8 Pareto Based Approach -- 6 A Variant of MOPSO -- 7 Comparative Study -- 7.1 Performance Evaluation -- 7.2 Conclusion and Future Work -- References -- Binary Ant Colony Optimization for Subset Problems -- 1 Introduction -- 2 Ant Colony Optimization -- 3 Feature Selection -- 4 Solution Representations for Subset Problems Using Ant Colony Optimization -- 5 Binary ACO -- 6 Computational Experiments -- 6.1 Fitness Function -- 6.2 Cross Validation (CV) -- 6.3 Datasets -- 6.4 Method -- 6.5 Datasets -- 6.6 Comparisons with Other Algorithms -- 7 Discussion -- 8 Conclusion -- 9 Future Work -- References -- Ant Colony for Locality Foraging in Image Enhancement -- 1 Introduction -- 2 Retinex Theory -- 3 Termite Retinex -- 4 Termite Retinex Locality -- 5 Termite Retinex Properties -- 5.1 Computational Complexity -- 5.2 Dynamic Range Stretching -- 5.3 Color Constancy and Color Correction -- 5.4 HDR Tone Rendering -- 6 Conclusions -- References -- Uncertainty Based Hybrid Particle Swarm Optimization Techniques and Their Applications -- 1 Introduction -- 2 Implementation of PSO -- 2.1 PSO Algorithm -- 3 Variants of PSO -- 3.1 Discrete Binary PSO (DBPSO) -- 3.2 Adaptive PSO -- 3.3 Multi-objective PSO -- 4 PSO in Feature Selection -- 5 PSO in Classification -- 5.1 Algorithm to Extract Best Classification Rule Begin -- 5.2 Rule Induction Algorithm -- 6 PSO in Hybrid Computing -- 6.1 Fuzzy PSO -- 6.2 Rough PSO -- 6.3 Rough Fuzzy PSO -- 7 Implementation and Results -- 8 Applications of PSO -- 9 Conclusion -- References -- Hybridization of Evolutionary and Swarm Intelligence Techniques for Job Scheduling Problem -- 1 Introduction -- 2 Evolutionary Algorithms -- 2.1 Genetic Algorithm. , 3 Swarm Intelligence -- 3.1 Ant Colony Optimization -- 3.2 Particle Swarm Optimization -- 3.3 Artificial Bee Colony Optimization -- 4 Related Work on Hybrid Techniques for Job Scheduling -- 4.1 Introduction -- 4.2 Experimental Results and Discussion -- 5 Proposed Hybrid Technique -- 5.1 Introduction -- 5.2 Adaptive ABC Algorithm -- 5.3 Adaptive ABC for Job Scheduling Problem -- 6 Experimental Results -- 6.1 Environment Infrastructure -- 6.2 Implementation and Results -- 7 Multiobjective Problems -- 8 Conclusion -- References.