DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. The state of each agent within the swarm is defined as its current position and function value within a design space and the agents learn to take favorable actions that maximize reward, which is based on the final value of the objective function. The proposed approach is tested on various benchmark optimization functions and compared to the performance of other global optimization strategies. Furthermore, the effect of changing the number of agents, as well as the generalization capabilities of the trained agents are investigated. The results show superior performance compared to the other optimizers, desired scaling when the number of agents is varied, and acceptable performance even when applied to unseen functions. On a broader scale, the results show promise for the rapid development of domain-specific optimizers

Numerical study of augmented lagrangian algorithms for constrained global optimization

To cite this article: Ana Maria A.C. Rocha & Edite M.G.P. Fernandes (2011): Numerical study of augmented Lagrangian algorithms for constrained global optimization, Optimization, 60:10-11, 1359-1378This article presents a numerical study of two augmented Lagrangian algorithms to solve continuous constrained global optimization problems. The algorithms approximately solve a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is solved by a population-based method that uses an electromagnetism-like (EM) mechanism to move points towards optimality. Three local search procedures are tested to enhance the EM algorithm. Benchmark problems are solved in a performance evaluation of the proposed augmented Lagrangian methodologies. A comparison with other techniques presented in the literature is also reported

Dynamic regional harmony search with opposition and local learning

ABSTRACT Harmony search (HS), as an emerging metaheuristic algorithm mimicking the musician's improvisation behavior, has demonstrated strong efficacy in solving various numerical and real-world optimization problems. To deal with the deficiencies in the original HS such as premature convergence and stagnation, a dynamic regional harmony search (DRHS) algorithm with opposition and local learning is proposed. DRHS utilizes opposition-based initialization, and performs independent harmony searches with respect to multiple groups created by periodically and randomly regrouping the harmony memory. Besides the traditional harmony improvisation operators, an opposition-based harmony creation scheme is used in DRHS to update each group memory. Any prematurely converged group will be restarted with its size being doubled to enhance exploration. Local search is periodically applied to exploit promising regions around topranked candidate solutions. The performance of DRHS has been evaluated and compared to the original HS using 12 numerical test problems taken from the CEC2005 benchmark. DRHS consistently outperforms HR on all test problems at both 10D and 30D. Harmony search (HS) [7]-[15], as an emerging metaheuristic algorithm, mimics the musicians' improvisation behavior. In HS, a candidate solution of an optimization problem corresponds to a musical harmony composed of notes played by a group of musicians. Each decision variable in a candidate solution is analogous to a musician with its value range analogized by the pitch range within which the corresponding musician plays the note. The quality of candidate solutions corresponds to the euphoniousness of musical harmonies. By simulating how a group of musicians keep enriching their experiences to collaboratively seek for the most euphonious harmony in the improvisation procedure, HS searches for global optima using harmony improvisation operators to iteratively evolve the harmony memory (HM) that consists of promising candidate solutions. Categories and Subject Descriptors HS has been successfully applied in a wide range of applications [8]- To address the above issues, we propose a dynamic regional harmony search (DRHS) algorithm incorporating opposition-based learning • Opposition-based learning is used to produce a HM that can better cover the entire solution space. • The HM is randomly split into multiple groups. Each group performs HS independently. The HM is periodically regrouped. During the searching, any prematurely converged group will be restarted with its size being doubled. This dynamic regional search scheme can force each group to independently exploit different sub-regions of the solution space while attempting to prevent both stagnation and premature convergence. • Each group first generates a new harmony using the original harmony improvisation operators. Then, an opposite harmony is created by applying the opposition-based learning to that new harmony with respect to the corresponding group. Among these two newly generated harmonies, the one with bette

Minimization of Active Power Loss and Voltage Profile Fortification by Using Differential Evolution – Harmony Search Algorithm

This paper presents DEHS (Differential Evolution-harmony Search) algorithm for solving the multi-objective reactive power dispatch problem .Harmony Search is a new heuristic algorithm, which mimics the procedure of a music player to search for an ideal state of harmony in music playing. Harmony Search can autonomously mull over each component variable in a vector while it generates a new vector. These features augment the flexibility of the Harmony Search algorithm and produce better solutions and overcome the disadvantage of Differential Evolution. Improved Differential Evolution method based on the Harmony Search Scheme, which we named it DEHS (Differential Evolution-harmony Search). The DEHS method has two behaviors. On the one hand, DEHS has the flexibility. It can adjust the values lightly in order to get a better global value for optimization. On the other hand,   DEHS can greatly boost the population’s diversity. It not only uses the DE’s strategies to search for global optimal results, but also utilize HS’s tricks that generate a new vector by selecting the components of different vectors randomly in the harmony memory and its outside. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.

Differential Evolution: A Survey and Analysis

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.http://dx.doi.org/10.3390/app810194

An intelligent computational approach to the optimization of inventory policies for single company

This study develops and tests a computational approach for determining optimal inventory policies for single company. The computational approach generally comprises of two major components: a meta-heuristic optimizer and an event-driven inventory evaluation module. Meta-heuristic is a powerful search technique, under the intelligent computational paradigm. The approach is capable of determining optimal inventory policy under various demand patterns regardless their distribution for a variety of inventory items. Two prototypes of perishability are considered: (1) sudden deaths due to disasters and (2) outdating due to expirations. Since every theoretical model is specially designed for a certain type of inventory problem while the real world inventory problems are numerous, it is desirable for the newly proposed computational approach to cover as many inventory problems/models as possible. In a way, the proposed meta-heuristic based approach unifies many theoretical models into one and beyond. Experimental results showed that the proposed approach provides comparable results to the theoretical model when demand follows their assumption. For demands not well conformed to the assumption, the proposed approaches are able to handle it but the theoretical approaches do not. This makes the proposed computational approach advantageous in that it can handle various types of real world demand data without the need to derive new models. The main motivation for this work is to bridge the gap between theory and practice so as to deliver a user-friendly and flexible computational approach for rationalizing the inventory control system for single company

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

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based meta-heuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm