Mobile Anchor-Assisted Localization Using Invasive Weed Optimization Algorithm

Sensor localization in wireless sensor networks has been addressed using mobile anchor (MA) and a metaheuristic algorithm. The path of a MA plays an important role in localizing maximum number of sensor nodes. The random and circle path planning methods have been presented. Each method has been evaluated for number of localized nodes, accuracy, and computing time in localization. The localization has been performed using trilateration method and two metaheuristic stochastic algorithms, namely invasive weed optimization (IWO) and cultural algorithm (CA). Experimental results indicate that the IWO-based localization outperforms the trilateration method and the CA-based localization in terms of accuracy but with higher computing time. However, the computing speed of trilateration localization is faster than the IWO- and CA-based localization. In the path-planning algorithms, the results show that the circular path planning algorithm localizes more nodes than the random path.

Population Diversity of Particle Swarm Optimization Algorithm on Solving Single and Multi-Objective Problems

Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: the exploration of new possibilities and the exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained.

Multi-Objective Optimal Power Flow of Integrated Renewable Systems Using a Novel Evolutionary Algorithm

This chapter introduces an approach to explain optimal power flow (OPF) for stochastic wind and conventional thermal power generators-based system. In this chapter, grasshopper optimization algorithm (GOA) is implemented to efficiently prove its superiority for solving wind-based OPF problem. Diminishing carbon emissions is a significant goal for the entire world; a tremendous penetration of unpredictable wind energy can assist in reducing emissions. In the previous decade, the access of renewable energy opening for energy production has improved significantly. WE has become an important source that has begun to be used for energy all over the world in recent years. The optimal dispatch between thermal and wind units to minimize the total generating costs and emission are considered as multi-objective (MO) model. In MO optimization, whole electrical energy generation costs and burning emissions are concurrently minimized. The performance of aforesaid approach is exercised and it proves itself as a superior technique as compared to other algorithms revealed in the literature.

Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) for Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with Pigeon Inspired Optimization, Simulated Annealing, Greedy Search, Hybrid Bee with Simulated Annealing, and Hybrid Bee with Greedy Search using BDeu score function as a metric for all algorithms. They investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of evaluations, the proposed algorithm achieves better performance than the other algorithms and produces better scores as well as the better values.

Analysis of the Dynamic Characteristics of the Firefly Algorithm

The firefly algorithm is a meta-heuristic algorithm, the fundamental principle of which mimics the characteristics associated with the blinking of natural fireflies. This chapter presents a rigorous analysis of the dynamics of the firefly algorithm, which the authors performed by applying a deterministic system that removes the stochastic factors from the state update equation. Depending on its parameters, the individual deterministic firefly algorithm exhibits chaotic behavior. This prompted us to investigate the relationship between the behavior of the algorithm and its parameters as well as the extent to which the chaotic behavior influences the searching ability of the algorithm.

Semi-Automatic Sensor Ontology Matching Based on Interactive Multi-Objective Evolutionary Algorithm

Since different sensor ontologies are developed independently and for different requirements, a concept in one sensor ontology could be described with different terminologies or in different context in another sensor ontology, which leads to the ontology heterogeneity problem. To bridge the semantic gap between the sensor ontologies, authors propose a semi-automatic sensor ontology matching technique based on an Interactive MOEA (IMOEA), which can utilize the user's knowledge to direct MOEA's search direction. In particular, authors construct a new multi-objective optimal model for the sensor ontology matching problem, and design an IMOEA with t-dominance rule to solve the sensor ontology matching problem. In experiments, the benchmark track and anatomy track from the Ontology Alignment Evaluation Initiative (OAEI) and two pairs of real sensor ontologies are used to test performance of the authors' proposal. The experimental results show the effectiveness of the approach.

Training Artificial Neural Networks With Improved Particle Swarm Optimization

Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is utilized to optimize the weights and bias of Artificial Neural Networks (ANNs). The trained ANNs then forecast electricity consumption in Thailand. The proposed algorithm reduces the forecasting error compared to the traditional training algorithms. The percentage reduction of error is 23.81% compared to the Backpropagation algorithm and 16.50% compared to the traditional PSO algorithm.

An Analysis of Fireworks Algorithm Solving Problems With Shifts in the Decision Space and Objective Space

Fireworks algorithms for solving problems with the optima shifts in the decision space and/or objective space are analyzed. The standard benchmark problems have several weaknesses in the research of swarm intelligence algorithms for solving single-objective problems. The optimum shift in decision space and/or objective space will increase the difficulty of problem-solving. Modular arithmetic mapping is utilized in the original fireworks algorithm to handle solutions out of the search range. The solutions are implicitly guided to the center of search range for problems with symmetrical search range via this strategy. The optimization performance of the fireworks algorithm on shift functions may be affected by this strategy. Four kinds of mapping strategies are compared with different problems. The fireworks algorithms with mapping to the boundary or mapping to a limited stochastic region obtain good performance on problems with the optimum shift.

Contour Gradient Optimization

Inspired by local cooperation in the real world, a new evolutionary algorithm, Contour Gradient Optimization algorithm (CGO), is proposed for solving optimization problems. CGO is a new type of population-based algorithm that emulates the cooperation among neighbors. Each individual in CGO evolves in its neighborhood environment to find a better region. Each individual moves with a velocity measured by the field of its nearest individuals. The field includes the attractive forces from its better neighbor in the higher contour level and the repulsive force from its worse neighbor in the lower contour level. In this chapter, CGO is compared with six different widely used optimization algorithms, and comparative analysis shows that CGO is better than these algorithms in respect of accuracy and effectiveness.

Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. Cold start problem is one of the prevailing issues in recommendation system where the system fails to render recommendation. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of user gender is less explored when compared with other information like age, profession, region, etc. In this chapter, genetic algorithm influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state-of-art approaches.