Metaheuristics are generic search strategies that can be adapted to solve complex problems. This paper describes in simple terms the most popular metaheuristics for combinatorial optimization problems. It also emphasizes the main contributions of the Canadian research community in the development and application of metaheuristics.

The topic of simulation–optimization has not been fundamentally tackled by many continuous-time modeling and simulation tools, yet. Common simulation-based optimization problems are usually coupled with standard optimization algorithms like any other simulation-free nonlinear optimization problems. While such couplings are usually based on many state-of-the-art software engineering concepts wit...

Real world problems are often of dynamic nature. They form a class of difficult problems that metaheuristics aim to solve. The goal is not only to attempt to find near-to optimal solutions for a defined objective function, but also to track them in the search space. We will discuss in this article the dynamic optimization in the continuous case. Then we will present the experimentation on a bat...

There has been considerable interest in the identification of structural properties of combinatorial problems that lead, directly or indirectly, to the development of efficient algorithms for solving them. One such concept is that of a backdoor set—a set of variables such that once they are instantiated, the remaining problem simplifies to a tractable form. While backdoor sets were originally d...

This paper presents a Cuckoo Optimization Algorithm (COA) model for the cost optimization of the one-way and two-way reinforced concrete (RC) slabs according to ACI code. The objective function is the total cost of the slabs including the cost of the concrete and that of the reinforcing steel. In this paper, One-way and two-way slabs with various end conditions are formulated as ACI code. The t...

We propose a novel approach for architecture selection and hidden neurons excitability improvement for the Extreme Learning Machine (ELM). Named Adaptive Number of Hidden Neurons Approach (ANHNA), the proposed approach relies on a new general encoding scheme of the solution vector that automatically estimates the number of hidden neurons and adjust their activation function parameters (slopes a...

This contribution addresses the task of computing optimal control trajectories for hybrid systems with switching dynamics. Starting from a continuous-time formulation of the control task we derive an optimization problem in which the system behavior is modelled by a hybrid automaton with linear discrete-time dynamics and discrete as well as continuous inputs. In order to transform the discrete ...

Many engineering design problems involve a combination of both continuous and discrete variables. However, the number of studies scarcely exceeds a few on mixed-variable problems. In this research Particle Swarm Optimization (PSO) algorithm is employed to solve mixedvariable nonlinear problems. PSO is an efficient method of dealing with nonlinear and non-convex optimization problems. In this pa...

In the first part of this paper, we present a unified framework for analyzing algorithmic complexity any optimization problem, whether it be continuous or discrete in nature. This helps to formalize notions like “input”, “size” and “complexity” context general mathematical optimization, avoiding dependent definitions which is one sources difference treatment within optimization. second employ l...

Euclidean norm computations over continuous variables appear naturally in the constraints or in the objective of many problems in the optimization literature, possibly defining non-convex feasible regions or cost functions. When some other variables have discrete domains, it positions the problem in the challenging Mixed Integer Nonlinear Programming (MINLP) class. For any MINLP where the nonli...