evolutionary techniques for multi-objective (MO) optimization are currently gaining significant attention from researchers in various fields due to their effectiveness and robustness in searching for a set of trade-of...
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evolutionary techniques for multi-objective (MO) optimization are currently gaining significant attention from researchers in various fields due to their effectiveness and robustness in searching for a set of trade-off solutions. Unlike conventional methods that aggregate multiple attributes to form a composite scalar objective function, evolutionary algorithms with modified reproduction schemes for MO optimization are capable of treating each objective component separately and lead the search in discovering the global Pareto-optimal front. The rapid advances of multi-objective evolutionary algorithms, however, poses the difficulty of keeping track of the developments in this field as well as selecting an existing approach that best suits the optimization problem in-hand. This paper thus provides a survey on various evolutionary methods for MO optimization. Many well-known multi-objective evolutionary algorithms have been experimented with and compared extensively on four benchmark problems with different MO optimization difficulties. Besides considering the usual performance measures in MO optimization, e.g., the spread across the Pareto-optimal front and the ability to attain the global trade-offs, the paper also presents a few metrics to examine the strength and weakness of each evolutionary approach both quantitatively and qualitatively. Simulation results for the comparisons are analyzed, summarized and commented.
The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of dir...
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The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.
Many robust design problems can be described by minimax optimization problems. Classical techniques for solving these problems have typically been limited to a discrete form of the problem. More recently, evolutionary...
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Many robust design problems can be described by minimax optimization problems. Classical techniques for solving these problems have typically been limited to a discrete form of the problem. More recently, evolutionary algorithms, particularly coevolutionary optimization techniques, have been applied to minimax problems. A new method of solving minimax optimization problems using evolutionary algorithms is proposed. The performance of this algorithm is shown to compare favorably with the existing methods on test problems. The performance of the algorithm is demonstrated on a robust pole placement problem and a ship engineering plant design problem.
This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied ...
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This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision varia
The global optimization of mixed integer non-linear problems (MINLP), constitutes a major area of research in many engineering applications. In this work, a comparison is made between an algorithm based on Simulated A...
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The global optimization of mixed integer non-linear problems (MINLP), constitutes a major area of research in many engineering applications. In this work, a comparison is made between an algorithm based on Simulated Annealing (M-SIMPSA) and two evolutionary algorithms: Genetic algorithms (GAs) and Evolution Strategies (ESs). Results concerning the handling of constraints, through penalty functions, with and without penalty parameter setting, are also reported. evolutionary algorithms seem a valid approach to the optimization of non-linear problems. Evolution Strategies emerge as the best algorithm in most of the problems studied. (C) 2001 Elsevier Science Ltd. All rights reserved.
In this work a procedure namely Findpeaks2 is proposed to detect the maximum sidelobe level (SLL) from the samples of three dimensional radiation pattern. This procedure detects all sidelobe peaks form the samples of ...
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In this work a procedure namely Findpeaks2 is proposed to detect the maximum sidelobe level (SLL) from the samples of three dimensional radiation pattern. This procedure detects all sidelobe peaks form the samples of the radiation pattern in the entire visible region. For illustration, a low sidelobe radiation pattern synthesis problem is formulated for two concentric regular hexagonal antenna array (CRHAA) geometries, having 6- and 8- rings. To verify the extent of applicability of the proposed procedure, both broadside and scanned array configurations are considered. Feed current amplitudes are considered as the optimizing variables. Two variations of current distributions are considered, i) identical feed for all the elements on a ring (hence the one variable per ring needs to be optimized), and ii) asymmetric excitation distribution (set of excitation amplitude of all elements as optimizing variables). The design objective has been considered to optimize the radiation patterns with very low interference from the entire sidelobe region. To restrict the fall of directivity value, a constraint on the lower limit of directivity value is considered. The impacts of symmetry and the constraint on directivity on the search of these algorithms are studied. evolutionary algorithms like Real Coded Genetic Algorithm (RGA), Firefly Algorithm (FFA), Flower Pollination Algorithm (FPA), an adaptive variant of Particle Swarm Optimization Algorithm namely (APSO), and two recently proposed variants of DE namely Exponentially Weighted Moving Average Differential Evolution (EWMA-DE), and Differential Evolution with Individual Dependent Mechanism (IDE) are employed for this pattern optimization problem.
The sale of electric energy generated by photovoltaic (PV) plants has attracted much attention in recent years. The installation of PV plants aims to obtain the maximum benefit of captured solar energy. The current me...
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The sale of electric energy generated by photovoltaic (PV) plants has attracted much attention in recent years. The installation of PV plants aims to obtain the maximum benefit of captured solar energy. The current methodologies for planning the design of the different components of a PV plant are not completely efficient. This paper addresses the optimization of the design of PV plants with solar tracking, which consists of the optimization of the variables that make up the PV plant to obtain the minimum electric (Joule) losses possible. These variables are the size and distribution of solar modules in the solar tracker, the distribution of the solar trackers in the field and the choice of inverter. evolutionary algorithms (EAs) are adaptive methods based on natural evolution that may be used for searching and optimization. Four different EAs have been used for optimizing the design of PV plants: steady-state genetic algorithm, generational genetic algorithm, CHC algorithm and DE algorithm. In order to test the performance of these algorithms we have used different proposed fields to mount PV plants. The results obtained show that EAs, and specifically DE with rand mutation schemes, are promising techniques to optimize design of PV plants. Furthermore, the results are contrasted with nonparametric statistical tests to support our conclusions. (c) 2012 Elsevier Ltd. All rights reserved.
This article proposes an evolutionary algorithm integrating Erdos-Renyi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population i...
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This article proposes an evolutionary algorithm integrating Erdos-Renyi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdos-Renyi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision -making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making.
A core operator of evolutionary algorithms (EAs) is the mutation. Recently, much attention has been devoted to the study of mutation operators with dynamic and non-uniform mutation rates. Following up on this area of ...
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A core operator of evolutionary algorithms (EAs) is the mutation. Recently, much attention has been devoted to the study of mutation operators with dynamic and non-uniform mutation rates. Following up on this area of work, we propose a new mutation operator and analyze its performance on the (1 + 1) evolutionary Algorithm (EA). Our analyses show that this mutation operator competes with pre-existing ones, when used by the (1 + 1) EA on classes of problems for which results on the other mutation operators are available. We show that the (1 + 1) EA using our mutation operator finds a (1/3)-approximation ratio on any non-negative submodular function in polynomial time. We also consider the problem of maximizing a symmetric submodular function under a single matroid constraint and show that the (1 + 1) EA using our operator finds a (1/3)-approximation within polynomial time. This performance matches that of combinatorial local search algorithms specifically designed to solve these problems and outperforms them with constant probability. Finally, we evaluate the performance of the (1 + 1) EA using our operator experimentally by considering two applications: (a) the maximum directed cut problem on real-world graphs of different origins, with up to 6.6 million vertices and 56 million edges and (b) the symmetric mutual information problem using a four month period air pollution data set. In comparison with uniform mutation and a recently proposed dynamic scheme, our operator comes out on top on these instances.
The most simple evolutionary algorithm (EA), the so-called (1+1) EA, accepts an offspring if its fitness is at least as large (in the case of maximization) as the Fitness of its parent. The variant (1+1)* EA only acce...
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The most simple evolutionary algorithm (EA), the so-called (1+1) EA, accepts an offspring if its fitness is at least as large (in the case of maximization) as the Fitness of its parent. The variant (1+1)* EA only accepts an offspring if its fitness Is strictly larger than the fitness of its parent. Here, two functions related to the class of long-path functions are presented such that the (1+1) EA maximizes one in polynomial time and needs exponential time for the other while the (1+1)* EA has the opposite behavior. These results demonstrate that small changes of an EA may change its behavior significantly. Since the (1+1) EA and the (1+1)* EA differ only on plateaus of constant fitness, the results also show how EAs behave on such plateaus. The (1+1) EA can pass a path of constant fitness and polynomial length in polynomial time. Finally, for these functions, it is shown that local performance measures like the quality gain and the progress rate do not describe the global behavior of EAs.
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