Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to ...

A novel approach is proposed for generating equations from measured data of dynamic processes. A composition of unary (alpha) and binary (beta) functions is represented by a real vector and adapted by an evolutionary algorithm to build mathematical ...

Improvement of processes in metallurgical industry is a constant of competitive enterprises, however, changes made in a process are risky and involves high cost and time, considering this, a model can be made even using inputs usually not presented in ...

Single-solution metaheuristics are among the earliest and most successful metaheuristics, with many variants appearing in the literature. Even among the most popular variants, there is a large degree of overlap in terms of actual behavior. Moreover, in ...

To many people, the terms nature-inspired algorithm and metaheuristic are interchangeable. However, this contemporary usage is not consistent with the original meaning of the term metaheuristic, which referred to something closer to a design pattern ...

In this paper, we tackle the distribution network expansion planning (DNEP) problem by employing two evolutionary algorithms (EAs): the classical Genetic Algorithm (GA) and a linkage-learning EA, specifically a Gene-pool Optimal Mixing Evolutionary ...

Conventionally control can be achieved by attempting to simplify complex dynamics. The field of morphological computation explores how mechanical complexity can be advantageous. In this paper we demonstrate morphological computation in tensegrity ...

Multi-modal problems with multiple local/global optima are ubiquitous in real-world application. Many multi-modal optimization algorithms have been developed to search as many local/global optima as possible. However, to locate and maintain many optima ...

Many-objective optimization problems are common in real-world applications, few evolutionary optimization methods, however, are suitable for them up to date due to their difficulties. We proposed a reference points-based evolutionary algorithm (RPEA) to ...

Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. ...

We have proposed the novel evolutionary computation called ``Dictyostelium based Genetic Algorithm" (DGA), which adopts the concept of life cycle of slime molds and can take a balance between exploitation and exploration.

In this research, we propose an ...

In this paper, we propose an Indicator-based Chemical Reaction Optimization (ICRO) algorithm for multiobjective optimization. There are two main motivations behind this work. On the one hand, CRO is a new recently proposed metaheuristic which ...

Artificial Bee Colony (ABC) algorithm, which was initially proposed for numerical function optimization, has been increasingly used for clustering. However, when it is directly applied to clustering, the performance of ABC is lower than expected. This ...

The present study introduces an automated mechanism to build algorithm portfolios for memetic algorithms. The objective is to determine an algorithm set involving combinations of crossover, mutation and local search operators based on their past ...

We propose a new collaborative guidance platform for a team of robots that should protect a fixed ground target from one or several threats. The team of robots performs high-level behaviors. These are hand-coded since they consist in driving the robots ...

Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to ...

Network design is a broad class of essential engineering and science problems. The target of network design is to construct a graph that satisfies some restrictions. Many network design problems (NDPs) are known as NP-hard and become more challenging as ...

Traffic assignment is a complex optimization problem. In case the road network has many links (thus a high number of alternative routes) and multiple origin-destination pairs, most existing solutions approximate the so-called user equilibrium (a variant ...