Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evolution. In contrast, when a multiobjective evolutionary algorithm is used, populations can be subject to individual-level objectives, team-level objectives, or combinations of the two. This paper explores the performance of cooperatively coevolved teams of agents controlled by artificial neural networks subject to these types of objectives. Specifically, predator agents are evolved to capture scripted prey agents in a torus-shaped grid world. Because of the tension between individual and team behaviors, multiple modes of behavior can be useful, and thus the effect of modular neural networks is also explored. Results demonstrate that fitness rewarding individual behavior is superior to fitness rewarding team behavior, despite being applied to a cooperative task. However, the use of networks with multiple modules allows predators to discover intelligent behavior, regardless of which type of objectives are used

Modified predator-prey (MPP) algorithm for single-and multi-objective optimization problems

The aim of this work is to develop an algorithm that can solve multidisciplinary design optimization problems. In predator-prey algorithm, a relatively small number of predators and a much larger number of prey are randomly placed on a two dimensional lattice with connected ends. The predators are partially or completely biased towards one or more objectives, based on which each predator kills the weakest prey in its neighborhood. A stronger prey created through evolution replaces this prey. In case of constrained problems, the sum o f constraint violations serves as an additional objective. Modifications of the basic predator-prey algorithm have been implemented in this study regarding the selection procedure, apparent movement of the predators, mutation strategy, dynamics of the Pareto convergence, etc. Further modifications have been made making the algorithm capable of handling equality and inequality constraints. The final modified algorithm is tested on standard constrained/unconstrained, single and multi-objective optimization problems

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals

Reference point based multi-objective optimization using evolutionary algorithms

Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Paretooptimal solution is also an important task which has received a lukewarm attention so far. In this paper, we combine one such preference-based strategy with an EMO methodology and demonstrate how, instead of one solution, a preferred set of solutions near the reference points can be found parallely. We propose two approaches for this task: (i) a modified EMO procedure based on the elitist non-dominated sorting GA or NSGAII [1] and (ii) a predator-prey approach based on original grid based procedure [2]. On two-objective to 10-objective optimization test problems, the modified NSGA-II approach shows its efficacy in finding an adequate set of Pareto-optimal points. On two and three-objective problems, the predator-prey approach also demonstrate its usefulness. Such procedures will provide the decision-maker with a set of solutions near her/his preference so that a better and a more reliable decision can be made

Environmental Economic Hydrothermal System Dispatch by Using a Novel Differential Evolution

This paper proposes the Novel Differential Evolution (NDE) method for solving the environmental economic hydrothermal system dispatch (EEHTSD) problem with the aim to reduce electricity generation fuel costs and emissions of thermal units. The EEHTSD problem is constrained by limitations on generations, active power balance, and amount of available water. NDE applies two modified techniques. The first one is modified mutation, which is used to balance global and local search. The second one is modified selection, which is used to keep the best solutions. When performing this modified selection, the proposed method completely reduces the impact of crossover by setting it to one. Moreover, the task of tuning this factor can be canceled. Original Differential Evolution (ODE), ODE with the first modification (MMDE), and ODE with the second modification (MSDE), and NDE were tested on two different hydrothermal systems for comparison and evaluation purposes. The performance of NDE was also compared to existing methods. It was indicated that the proposed NDE is a very promising method for solving the EEHTSD problem

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems

This thesis presents a new class of evolutionary algorithms called mobile cellular evolutionary algorithms (mcEAs). These algorithms are characterized by individuals moving around on a spatial population structure. As a primary objective, this thesis aims to show that by controlling the population density and mobility in mcEAs, it is possible to achieve much better control over the rate of convergence than what is already possible in existing cellular EAs. Using the observations and results from this investigation into selection pressure in mcEAs, a general architecture for developing agent-based evolutionary algorithms called Artificial Ecosystems (AES) is presented. A simple agent-based EA is developed within the scope of AES is presented with two individual-based bottom-up schemes to achieve dynamic population sizing. Experiments with a test suite of optimization problems show that both mcEAs and the agent-based EA produced results comparable to the best solutions found by cellular EAs --Abstract, page iii

Parallel predator-prey interaction for evolutionary multi-objective optimization

International audienceOver the last decade, the predator-prey model (PPM) has emerged as an alternative algorithmic approach to multi-objective evolutionary optimization, featuring a very simple abstraction from natural species interplay and extensive parallelization potential. While substantial research has been done on the former, we for the first time review the PPM in the light of parallelization: We analyze the architecture and classify its components with respect to a recent taxonomy for parallel multi-objective evolutionary algorithms. Further, we theoretically examine benefits of simultaneous predator collaboration on a spatial population structure and give insights into solution emergence. On the prey level, we integrate a gradient-based local search mechanism to exploit problem independent parallelization and hybridize the model in order to achieve faster convergence and solution stability. This way, we achieve a good approximation and unfold further parallelization potential for the model

Multicriteria conditional optimization based on genetic algorithms

This article takes on solving the problem of multicriteria conditional optimization. This problem is one of the most key tasks of the current time and has its application in many areas. Reuse of various existing algorithms for solving unconstrained optimization is proposed. Different methods of multicriteria unconditional optimization are reviewed. The advantages and disadvantages of each algorithm are analyzed. The algorithms modified to take into account the constraints. Additional algorithms of transition from solving an unconditional optimization problem to a conditional optimization problem are developed. A genetic algorithm SPEA2 was used to test the developed algorithms. Examples of solving the problem at hand using the aforementioned algorithms are presented. A comparative analysis of the final results was conducted

Ecosystem evolution mechanism of manufacturing service system driven by service providers

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.To date, research has mainly discussed the definition and classification of producer services. Yet many companies require an integrated solution of products and producer services. The aim of this paper is to propose a model of the ecological evolution of manufacturing service systems (MSS) driven by service providers. This model adopts modular thinking to integrate dispersive producer services into an entire MSS, the process of which forms the ecological evolution of MSS. This paper applies ecological theory to build the ecological evolution mechanism of MSS. In this mechanism, the predator–prey relationship is used as a metaphor for the relationships between manufacturers and service providers in a MSS. A predator–prey model is built to simulate the ecological evolution of MSS. A predation cellular genetic algorithm is adopted to optimise this evolution. The proposed model and algorithm are demonstrated and validated by a case study of MSS in bicycle industry. This study contributes to theory by proposing and clarifying the mechanism for provision of services between businesses. The future research will focus on the application and empirical analysis of this mechanism derived from big data.This work has been supported by the National Natural Science Foundation of China [grant number 51205353], [grant number 71571161], [grant number 51475434]