Stephen Chen

York University · School of Information Technology

The goal of exploration to produce diverse search points throughout the search space can be countered by the goal of selection to focus search around the fittest current solution(s). In the limit, if all exploratory search points are rejected by selection, then the behaviour of the metaheuristic will be equivalent to one which performs no exploratio...

Finding good solutions on multi-modal optimization problems depends mainly on the efficacy of exploration. However, many search techniques applied to multi-modal problems were initially conceptualized with unimodal functions in mind, prioritizing exploitation over exploration. In this paper, we perform a study on the efficacy of exploration under r...

Please see the full version of this paper: https://www.researchgate.net/publication/363327919_Methods_to_Detect_and_Address_Stall_in_Particle_Swarm_Optimization

Restarts are a popular remedy to address (premature) convergence in metaheuristics. In Particle Swarm Optimization , it has been observed that swarms often "stall" as opposed to "converge". A stall occurs when all of the forward progress that could occur is instead rejected as failed exploration. Since the swarm is in a good region of the search sp...

As software systems grow in size and complexity, the process of configuring them to meet individual needs becomes more and more challenging. Users, especially those that are new to a system, are faced with an ever increasing number of configuration possibilities, making the task of choosing the right one more and more daunting. However, users are r...

Renewable energy penetration can be increased by the use of IoT connected devices. The focus of optimization should be less on individual devices, but rather on the networked effect of millions of devices together. By implementing global optimization on electrical loads, it may be possible to eliminate the need for fossil fuel-based electricity gen...

Exploration and exploitation are analyzed in Particle Swarm Optimization (PSO) through a set of experiments that make new measurements of these key features. Compared to analyses on diversity and particle trajectories, which focus on particle motions and their potential to achieve exploration and exploitation, our analysis also focuses on the pbest...

In this article we consider a difficult combinatorial optimization problem arising from the operation of a system for testing electronic circuit boards (ECB). This problem was proposed to us by a company that makes a system for testing ECBs and is looking for an efficient way of planning the tests on any given ECB. Because of its difficulty, we fir...

Results for an implementation of Leaders and Followers on the CEC2013 Real-Parameter Optimization Benchmark Functions. Source code for Leaders and Followers: https://www.researchgate.net/publication/281827268_Leaders_and_Followers_%28Matlab_code%29?ev=prf_pub

Large-scale global optimization is a challenging task which is embedded in many scientific and engineering applications. Among large scale problems, multimodal functions present an exceptional challenge because of the need to promote exploration. In this paper we present a hybrid heuristic specifically designed for optimizing large scale multimodal...

When optimizing multi-modal spaces, effective search techniques must carefully balance two conflicting tasks: exploration and exploitation. The first refers to the process of identifying promising areas in the search space. The second refers to the process of actually finding the local optima in these areas. This balance becomes increasingly import...

A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attrac...

The existence of the curse of dimensionality is well known, and its general effects are well acknowledged. However, and perhaps due to this colloquial understanding, specific measurements on the curse of dimensionality and its effects are not as extensive. In continuous domains, the volume of the search space grows exponentially with dimensionality...

Minimum Population Search is a new metaheuristic specifically designed for optimization of multi-modal problems. Its core idea is to guarantee full coverage of the search space with the smallest possible population. A small population increases the chances of convergence and the efficient use of function evaluations, but it can also induce the risk...

The existence of the curse of dimensionality is well known, and its general effects are well acknowledged. However, perhaps due to this colloquial understanding, specific measurements on the curse of dimensionality and its effects are not as extensive. In continuous domains, the volume of the search space grows exponentially with dimensionality. Co...

Multi-modal optimization involves two distinct tasks: identifying promising attraction basins and finding the local optima in these basins. Unfortunately, the second task can interfere with the first task if they are performed simultaneously. Specifically, the promise of an attraction basin is often estimated by the fitness of a single sample solut...

Minimum Population Search is a new metaheuristic specifically designed for optimizing multi-modal problems. Its core idea is to guarantee exploration in all dimensions of the search space with the smallest possible population. A small population increases the chances of convergence and the efficient use of function evaluations – an important consid...

Computer modeling of protein-ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problem...

Optimisation in multimodal landscapes involves two distinct tasks: identifying promising regions and location of the (local) optimum within each region. Progress towards the second task can interfere with the first by providing a misleading estimate of a region's value. Thresheld convergence is a generally applicable "meta"-heuristic designed to co...

One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of algorithms dramatically, while it highly increase...

Results for an implementation of Minimum Population Search on the CEC2013 Real-Parameter Optimization Benchmark Functions. Source codes: https://www.researchgate.net/publication/262318460_Minimum_Population_Search_%28MATLAB_code%29?ev=prf_pub https://www.researchgate.net/publication/274567146_MPS_for_CEC2013_%28C_code%29?ev=prf_pub

An implementation of the Univariate Marginal Distribution Algorithm with results available for the CEC2013 Real-Parameter Optimization Benchmark Functions.

Results for an implementation of the Univariate Marginal Distribution Algorithm on the CEC2013 Real-Parameter Optimization Benchmark Functions

Results for an implementation of Standard Particle Swarm Optimization on the CEC2013 Real-Parameter Optimization Benchmark Functions This revised version is based on fixing a code error in the previous version. See the Appendix for more details. Source code: https://www.researchgate.net/publication/259643342_Source_code_for_an_implementation_of_S...

An implementation of Standard Particle Swarm Optimization with results available for the CEC2013 Real-Parameter Optimization Benchmark Functions Fixes a typo -- see the Appendix in the tech report for more information.

Results for an implementation of Standard Particle Swarm Optimization on the CEC2013 Real-Parameter Optimization Benchmark Functions See the (revised) tech report for more information.

El cómputo de alto rendimiento es una necesidad para el desarrollo de investigaciones con grandes volúmenes de datos. La creciente demanda de este tipo de resultados ha impulsado a varios centros de investigación a poner en funcionamiento recursos de cómputo de alto rendimiento. En Cuba no existe una solución definitiva que permita a todos los cent...

PLEASE see the REVISION -- This version contains an ERROR. For the corrected code, please see https://www.researchgate.net/publication/259643342_Source_code_for_an_implementation_of_Standard_Particle_Swarm_Optimization_--_revised For more information, please see the Appendix in the revised tech report: https://www.researchgate.net/publication/25...

Due to an ERROR in the used code, a revised set of results is now available. Please see https://www.researchgate.net/publication/259643350_PSO_results_--_CEC2013_Real-Parameter_Optimization_Benchmark_Functions_--_revised

A revised version updates an ERROR in the used code. Please see https://www.researchgate.net/publication/259643271_Standard_Particle_Swarm_Optimization_on_the_CEC2013_RealParameter_Optimization_Benchmark_Functions_--_revised

Reimplementation of final version of PSO with Thresheld Convergence for follow-up research. No major variations from published results expected.

Many heuristic search techniques have concurrent processes of exploration and exploitation. In particle swarm optimization, an improved pbest position can represent a new more promising region of the search space (exploration) or a better solution within the current region (exploitation). The latter can interfere with the former since the identific...

During the search process of differential evolution (DE), each new solution may represent a new more promising region of the search space (exploration) or a better solution within the current region (exploitation). This concurrent exploitation can interfere with exploration since the identification of a new more promising region depends on finding...

Population-based heuristics can be effective at optimizing difficult multi-modal problems. However, population size has to be selected correctly to achieve the best results. Searching with a smaller population increases the chances of convergence and the efficient use of function evaluations, but it also induces the risk of premature convergence. L...

In computer game software, the implementation of simulated urban crowds is widespread. Representation of pedestrians in computer games has, to date, lagged behind what has been shown possible in academic studies and simulation software. Primary reasons for this are the strict CPU budgets that game AI has to function under, and algorithm implementat...

This looks like the last version used for data collection, but no guarantees.

Multi-swarm systems base their search on multiple sub-swarms instead of one standard swarm. The use of diverse sub-swarms increases performance when optimizing multi-modal functions. However, new design decisions arise when implementing multi-swarm systems such as how to select the initial positions and initial velocities, and how to coordinate the...

Particle swarm optimization can be viewed as a system with two populations: a population of current positions and a population of personal best attractors. In genetic algorithms, crossover is applied after selection – the goal is to create a new offspring solution using components from the best available solutions. In a particle swarm, the best ava...

Stochastic search techniques for multi-modal search spaces require the ability to balance exploration with exploitation. Exploration is required to find the best region, and exploitation is required to find the best solution (i.e. the local optimum) within this region. Compared to hill climbing which is purely exploitative, simulated annealing prob...

Differential evolution (DE) is a widely-effective population-based continuous optimiser that requires convergence to automatically scale its moves. However, once its population has begun to converge its ability to conduct global search is dimin-ished, as the difference vectors used to generate new solutions are derived from the current population m...

This paper describes a novel problem formulation and specialised Multi- Objective Particle Swarm Optimisation (MOPSO) algorithm to discover the reaction pathway and Transition State (TS) of small molecules. Transition states play an important role in computational chemistry and their discovery represents one of the big challenges in computational c...

Particle swarm optimization cannot guarantee convergence to the global optimum on multi-modal functions, so multiple swarms can be useful. One means to coordinate these swarms is to use a separate search mechanism to identify different regions of the solution space for each swarm to explore. The expectation is that these independent sub-swarms can...

Each particle in a swarm maintains its current position and its personal best position. It is useful to think of these personal best positions as a population of attractors – updates to current positions are based on attractions to these personal best positions. If the population of attractors has high diversity, it will encourage a broad explorati...

We describe an algorithm that explores potential energy surfaces (PES) and finds approximate reaction paths and transition states. A few (≈6) evolving atomic configurations ("climbers") start near a local minimum M1 of the PES. The climbers seek a shallow ascent, low energy, path toward a saddle point S12, cross over to another valley of the PES, a...

Standard particle swarm optimization cannot guarantee convergence to the global optimum in multi-modal search spaces, so multiple swarms can be useful. The multiple swarms all need initial positions and initial velocities for their particles. Several simple strategies to select initial positions and initial velocities are presented. A series of exp...

Exploration and exploitation are two important factors to consider in the design of optimization techniques. Two new techniques are introduced for particle swarm optimization: “resets” increase exploitation and “delayed updates” increase exploration. In general, the added exploitation with resets helps more with the lbest topology which is more exp...

Market bubbles often occur around the same time that new means of investing become available to enable increased market participation. An important aspect of increased market participation is the possible introduction of new investors who behave differently from existing traditional investors. Preliminary evidence from a new data set constructed fr...

A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr ϵ [0, 1]. While very low values are recommended for and used with separable problems, on non-separable problems, which include most real-world problems, Cr = 0.9 has become the de facto standard, working well across a large range of problem domains. Rece...

Locust swarms are a multi-optima particle swarm that have the ability to exploit lower-dimensional searches. These lower-dimensional searches can exploit separable problems and in the limit solve them one dimension at a time. Experiments on the Black-Box Optimization Benchmarking (BBOB) problems show that lower-dimensional searches also play an imp...

A key challenge for many heuristic search techniques is scalability - techniques that work well on low-dimension problems may perform poorly on high-dimension problems. To the extent that some problems/problem domains are separable, this can lead to a benefit for search techniques that can exploit separability. The standard algorithm for particle s...

A standard means of testing an economic/financial model Is to see if its simulation can reproduce quantitatively observed phenomena. It is generally easier to produce quantitative results from quantitative models, so qualitative models are often less highly regarded - they are more difficult to test and verify. The hypothesis that social investors...

The evaluation of an organization’s environmental performance is an integral part of a corporate environmental management information system. This chapter considers an organization’s environmental impact assessment with respect to a water resource. It investigates formal approaches to the development of temporal monitoring designs for producing dat...

Locust Swarms are a recently-developed multi-optima particle swarm. To test the potential of the new technique, they have been applied to the 1000-dimension optimization problems used in the recent CEC2008 Large Scale Global Optimization competition. The results for Locust Swarms are competitive on these problems, and in particular, much better tha...

Locust Swarms are a newly developed multi-optima particle swarm. They were explicitly developed for non-globally convex search spaces, and their non-convergent search behaviours can also be useful for problems with fractal and fractured landscapes. On the 1000-dimensional “FastFractal” problem used in the 2008 CEC competition on Large Scale Global...

Locust swarms are a new multi-optima search technique explicitly designed for non-globally convex search spaces. They use ldquosmartrdquo start points to scout for promising new areas of the search space before using particle swarms and a greedy local search technique (e.g. gradient descent) to find a local optimum. These scouts start a minimum dis...

The number of observations is limited by budgetary and other constraints, but the collected data must be sufficient to meet a wide range of monitoring objectives. The process of determining an efficient temporal water quality monitoring design has been formulated as an optimization problem. In the problem, a target level of acceptable uncertainty f...

Economic models attempt to represent the aggregate behaviour of individual actors engaged in an economic activity. To ensure tractability in the subsequent mathematics, restrictive assumptions on the modelled phenomena are often required. Economic features that cannot be modelled mathematically can be difficult to verify experimentally. The hybrid...

Best Practice Guidelines (BPGs) represent a promising way to improve nursing care by reducing the time lag between research findings and subsequent changes in healthcare practices. Translating paper-based BPGs into a portable, computer-based format is seen as an important step towards the widespread use of BPGs in nursing practice. In the process o...

An inherent assumption in many search techniques is that information from existing solution(s) can help guide the search process to find better solutions. For example, memetic algorithms can use information from existing local optima to effectively explore a globally convex search space, and genetic algorithms assemble new solution candidates from...

Continuum Probe DesignerTM by Acoustic Ideas Inc. is a tool that can help design the “best” phased array ultrasonic transducer for a given inspection task. Given a specific surface geometry for the ultrasonic transducer, one component of Continuum Probe DesignerTM can determine the number of elements, and the required size and shape of each element...

Existing models of financial market prices typically assume that investors are informed with economic data and that wealth maximization motivates them. This paper considers the social dimensions of investing and the effect that this additional motivation has on the evolution of prices in a multi-agent model of an equity market. Agents in this model...