Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

Efficient Algorithms for Solving Size-Shape-Topology Truss Optimization and Shortest Path Problems

Efficient numerical algorithms for solving structural and Shortest Path (SP) problems are proposed and explained in this study. A variant of the Differential Evolution (DE) algorithm for optimal (minimum) design of 2-D and 3-D truss structures is proposed. This proposed DE algorithm can handle size-shape-topology structural optimization. The design variables can be mixed continuous, integer/or discrete values. Constraints are nodal displacement, element stresses and buckling limitations. For dynamic (time dependent) networks, two additional algorithms are also proposed in this study. A heuristic algorithm to find the departure time (at a specified source node) for a given (or specified) arrival time (at a specified destination node) of a given dynamic network. Finally, an efficient bidirectional Dijkstra shortest path (SP) heuristic algorithm is also proposed. Extensive numerical examples have been conducted in this study to validate the effectiveness and the robustness of the proposed three numerical algorithms

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Multidisciplinary and Multi-Objective Optimal Design of a Cascade Control System for a Flexible Wing with Embedded Control Surfaces Having Actuator Dynamics

A multidisciplinary and multi-objective optimization approach that integrates the design of the control surfaces’ sizes, active control systems, and estimator for an aircraft’s wing with three control surfaces is developed. Due to its attractive stability robustness properties, a control system based on the LQR (Linear Quadratic Regulator) is built for each control surface. The geometrical parameters of the control surfaces such as the span wise and chord lengths, the design details of the LQR penalty matrices, and the locations of the estimator poles are tuned by a widely used multi-objective optimization algorithm called NSGA-II (Non-dominated Sorting Genetic Algorithm). Four objectives are considered: minimizing impacts of external gust loads, maximizing stability robustness and extending flutter boundaries, reducing control energy consumption, and minimizing the Frobenius norm of the estimator gains. The solution of the multi-objective optimization problem is a set called Pareto set and the set of the corresponding function evaluation is called Pareto front. The solution set contains various geometrical configurations of the control surfaces with different feedback gains, which represent different degrees of optimal compromises among the design objectives. The optimization results demonstrate the competing relationship between the design objectives and necessity of handling the design problem in a multidisciplinary and multi-objective context. Three major results are obtained from inspecting the profiles of the closed-loop eigenvalues at various airspeeds 1) a unique control gain can be designed for the entire flight envelope, 2) the flutter boundaries can be infinitely extended, and 3) a unique observer gain can be designed for the entire flight envelope. The third chapter of this thesis presents a multi-objective and multidisciplinary optimal design of a cascade control system for an aircraft wing with four aerodynamic ailerons actuated by four identical brushless DC motors. The design of the control system is broken into a secondary and primary control algorithm. The primary control algorithm is designed based on the concept of LQR and then applied to mathematical model of the wing and its control surfaces to calculate their required deflections. The output of the primary controller serves as set-point for the secondary control loop which consists of the dynamic of the DC motor and Proportional Velocity (PV) based controller. Then, an optimal design of the control algorithms is carried out in multi-objective and multidisciplinary settings. Three objectives are considered: 1) the speed of response of the secondary controlled system must be faster than that of the primary one, 2) the controlled system must be robust against external disturbances affecting both control layers, and 3) optimal energy consumption. The decision variables of the primary as well as secondary control algorithms and the sizing elements of the control surfaces form the design parameter space of the optimization problem. Both geometrical and dynamic constraints are applied on the setup parameters. The multi-objective optimization problem (MOP) is solved by NSGA-II, which is one of the popular algorithms in solving MOPs. The solution of the MOP is a set of optimal control algorithms that represent the conflicts among the design objectives. Numerical simulations show that the design goals are achieved, the secondary control is always fast enough to prevent the propagation of disturbances to the primary loop, the inner and outer control algorithms are robust against disturbance inputs, and the primary control loop stays stable when the air stream velocity varies from 80 to 1000 (⁄) even at its worst relative stability value. The presented study may become the basis for multi-objective and multidisciplinary optimal design for aeroelastic structure having actuator dynamics