In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a trigger...

Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal solutions for continuous optimization problems. Updating kinetic equations for particle swarm optimization algorithm are improved to solve traveling salesman problem (TSP) based on problem characteristics and discrete variable. Those strategies which are named heuristic factor, reversion mutant and adaptive no...

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

this study proposes a modified version of cultural algorithms (cas) which benefits from rule-based system for influence function. this rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. this is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. this rule ...

A dynamic system is represented as a set of equations that specify how variables change over time. The equations in the system specify how to compute the new values of the state variables as a function of their current values and the values of the control parameters. If those parameters change beyond certain values, the system exhibits qualitative changes in its behavior. Those qualitative chan...

The purpose of this paper is to evaluate the performance of two approaches based on Vector Evaluated Particle Swarm Optimization (VEPSO) algorithm in two real world applications, which are the environmental economic dispatch problem and the optimization of a reinsurance contract portfolio. The two tested algorithms are the canonical VEPSO and a new version called VEPSO-N, where in the last one ...

Economic dispatch is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents an over view of three optimization algorithms namely real coded genetic algorithm, particle swarm opti...

This paper presents a particle swarm ant colony optimization for design of truss structures. The algorithm is based on the particle swarm optimizer with passive congregation and ant colony optimization. The particle swarm ant colony optimization applies the particle swarm optimizer with passive congregation for global optimization and ant colony approach is employed to update positions of parti...

Particle swarm optimization (PSO) is a Swarm Intelligence technique used for optimization motivated by the social behavior of individuals in large groups in nature. The damped mass-spring analogy known as the PSO continuous model allowed us to derive a whole family of particle swarm optimizers with different properties with regard to their exploitation/exploration balance. Using the theory of s...

In this paper, we present an extension of the heuristic called “particle swarm optimization” (PSO) that is able to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle and is based on the idea of having a set of subswarms instead of single particles. In each sub-swarm, a PSO algorithm is executed and, a...