Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem...

MANET’s use wireless medium for communication, thus two nodes arranged in a multi-hop fashion can communicate directly if and only if they are within their transmission radius. MANET‘s have adopted many conventional routing algorithms, swarm intelligence (SI) has recently captured much attention because many new researches have proposed its adoption for MANET routing. Many researchers have prop...

PSO is a robust and stochastic optimization technique based on the movement and the intelligence of the swarms. PSO is inspired with social behavior of bird flocking and fish schooling. PSO algorithm is applied to solve the real world complex optimization problems. So many real world optimization problems are solved by using different combinations and variations of PSO algorithms. In this paper...

Metaheuristic algorithms, especially those based on swarm intelligence (SI), form an important part of contemporary global optimization algorithms (Kennedy and Ebarhart, 1995; Yang, 2008; Auger and Teytaud, 2010; Auger and Doerr, 2010; Blum and Roli, 2003; Neumann and Witt 2010; Parpinelli and Lopes, 2011). Good examples are particle swarm optimization (PSO) (Kennedy and Eberhart, 1995) and fir...

Hard optimization problems that cannot be solved within reasonable time by standard, mathematical, deterministic methods are of great practical interest. Metaheuristics inspired by nature were recently successfully used for such problems. These metaheuristics are based on random Monte-Carlo search guided by simulation of some nature intelligence, especially evolution and swarm intelligence. One...

Human Group Optimization (HGO) algorithm, derived from the previously proposed seeker optimization algorithm (SOA), is a novel swarm intelligence algorithm by simulating human behaviors, especially human searching/foraging behaviors. In this paper, a canonical HGO with local search (L-HGO) is proposed. Based on the benchmark functions provided by CEC2005, the proposed algorithm is compared with...

Particle Swarm Optimisation (PSO) is an intelligent search method based on swarm intelligence and has been widely used in many fields. However it is also easily trapped in local optima. In this paper, we propose two hybrid PSO algorithms: one uses a Differential Evolution (DE) operator to replace the standard PSO method for updating a particle’s position; and the other integrates both the DE op...

This paper analyzes the collective behaviors of swarm robots that play role in the aggregation scenario. Honeybee aggregation is an inspired behavior of young honeybees which tend to aggregate around an optimal zone. This aggregation is implemented based on variation of parameters values. In the second phase, two modifications on original honeybee aggregation namely dynamic velocity and compara...

In this research paper, we focused on the developing a secure and efficient time-series forecasting of nuclear reactions using swarm intelligence (SI) algorithm. Nuclear radioactive management time series for casting is problem to be addressed if power deliver major part our energy consumption. This explains how SI processing techniques can used automate accurate reaction forecasting. The goal ...