Largescale global optimization through consensus of opinions over complex networks
 Omid AskariSichani^{1} and
 Mahdi Jalili^{1}Email author
DOI: 10.1186/21943206111
© Sichani and Jalili; licensee Springer. 2013
Received: 31 October 2012
Accepted: 28 March 2013
Published: 24 April 2013
Abstract
Purpose
Largescale optimization tasks have many applications in science and engineering. There are many algorithms to perform such optimization tasks. In this manuscript, we aim at using consensus in multiagent systems as a tool for solving largescale optimization tasks.
Method
The model is based on consensus of opinions among agents interacting over a complex networked structure. For each optimization task, a number of agents are considered, each with an opinion value. These agents interact over a networked structure and update their opinions based on their bestmatching neighbor in the network. A neighbor with the best value of the objective function (of the optimization task) is referred to as the bestmatching neighbor for an agent. We use structures such as pure random, smallworld and scalefree networks as interaction graph. The optimization algorithm is applied on a number of benchmark problems and its performance is compared with a number of classic methods including genetic algorithms, differential evolution and particle swarm optimization.
Results
We show that the agents could solve various largescale optimization tasks through collaborating with each other and getting into consensus in their opinions. Furthermore, we find pure random topology better than smallworld and scalefree topologies in that it leads to faster convergence to the optimal solution. Our experiments show that the proposed consensusbased optimization method outperforms the classic optimization algorithms.
Conclusion
Consensus in multiagents systems can be efficiently used for largescale optimization problems. Connectivity structure of the consensus network is effective in the convergence to the optimum solution where random structures show better performance as compared to heterogeneous networks.
AMS subject classification
15A04, 54A20, 60J20, 92D25
Keywords
Largescale optimization Complex networks Continuous opinion formation Consensus Scalefree networks Smallworld networksBackground
Networks are everywhere and we confront many networks in our daily life; they are practically present where any kind of information is transmitted or exchanged. Networks such as the Internet, the World Wide Web, engineering, social, biological and economical networks have been subject to heavy studies in the last decade and many applications have been developed based on network science Albert & Barabasi (2002, 1999; Barabasi & Albert 1999; Boccaletti et al. 20062006; Newman 2003; Newman & Park 2003; Strogatz 20011998). The progress in network science accelerated after the seminal work of Watts & Strogatz (1998) on collective behavior of smallworld networks Watts & Strogatz (1998) and Barabasi & Albert (1999) on scalefree graphs (Barabasi & Albert 1999). Watts and Strogatz discovered that many realworld networks have smallworld property in that their characteristics path length scales logarithmically with network size (Watts & Strogatz 1998) – a property that is observed in random networks. At the same time, these networks show high levels of transitivity (clustering coefficient) (Watts & Strogatz 1998) – much higher than corresponding random networks. Furthermore, many real networks from different disciplines were shown to have a powerlaw degree distribution (Barabasi & Albert 1999); the probability of having a node with degree k is k^{γ} with γ being in the range 2–3. Real networks have been shown to have more complex properties such as motifs (Milo et al. 2002) and community structure (Girvan & Newman 2002). These structural features influence dynamics and functionality of networks. For example, synchronization and consensus properties of networks largely depend on their structure (Belykh et al. 2005; Lu et al. 2004).
The most striking pattern of networked structures appear when a number of agents (each with simple behavior) interact leading to complex behaviors as a result of collective motion. Synchronization of interacting agents  as the most striking form of collective behavior  has many applications in science and engineering. For example, techniques available in network theory can be used for efficient distrusted inference in sensor networks (2007; Scutari et al. 2008). In this work we used tools available in network science to perform a numerical optimization task. Optimization is an approach that iteratively improves the performance of a system, which is formulated as a single standard measurement equation called cost (or objective) function.
In order to use network theory for solving an optimization task, we used the concept of consensus formation in the opinions of multiagent systems. Let us consider a network of agents where a (discrete or continuous) opinion value is associated to each agent. Agents can influence each others’ opinions through the connections existing between them, i.e., the edges of the network. Considering some simple update rules and if certain conditions are met, the agents can reach a consensus in their opinions through a number of opinion updates (Kozma & Barrat 2008; Carletti et al. 2006). In this work we considered the evolution of continuous opinions based on the modified version of bounded confidence model (Deffuant et al. 2001), which has been extensively studied in recent years (Gandica et al. 2010; Weisbuch 2004; Urbig et al. 2008). The previous studies of continuous opinion formation have been mainly performed considering uniform agents (Gandica et al. 2010; Weisbuch 2004; Urbig et al. 2008). However, in reality the agents are diverse in their wealth and social status, and hence, have diverse influence on others (Holyst et al. 2001; Lewenstein et al. 1992; Jalili 2013a; Jalili 2013b). Therefore, we associated specific weight for each agent resulting in faster consensus.
The paradigm proposed in this manuscript was applied on a number of benchmark problems. We first considered a simple function with many local optima and showed that the proposed optimization strategy could successfully find the optimum. We then applied the method on a number of benchmark problems from CEC 2010 competition benchmark set (Tang et al. 2009). We compared the performance of the proposed consensusbased optimization approach with that of a number of classic optimization methods including genetic algorithms, differential evolution and particle swarm optimization.
Methods
Optimization through consensus in the network
The paradigm we have proposed for largescale optimization task is based on consensus in networked structures. In opinion formation models, there is a population of agents, each with a (discrete or continuous) opinion value representing its information about a subject (Deffuant et al. 2001; Gandica et al. 2010; Weisbuch 2004; Urbig et al. 2008). The term opinion is not easy to define in reality; however, it can be considered as a discrete or continuous value expressing the individuals’ degree of desire or preference. This opinion is often represented as a real number when the model is unimodal or as a vector of real numbers when the model is multimodal. In this paper we aimed at optimizing an objective function, and therefore, each agent will have an opinion value containing all the input parameters of desired objective function, i.e., a multimodal model.
Opinion formation in multiagent systems
The agents update their opinions as a result of interactions with their neighboring agents. Consider two neighboring agents i and j with opinions as x_{ i } and x_{ j }, respectively. Their opinions at time n + 1 will be a function of their previous opinions, i.e. x_{ i }(n + 1) = f_{1}(x_{ i }(n), x_{ j }(n)), x_{ j }(n + 1) = f_{2}(x_{ i }(n), x_{ j }(n)). If certain conditions are met, after a number of updates in these values, the agents can reach a consensus in their opinions (Kozma & Barrat 2008; Carletti et al. 2006). The collective behavior of the agents over complex networks largely depends on the structural properties of the networks (Amblard & Deffuant 2004), and minor modification in the structure of the network can have drastic effects on the behavior of opinion formation (Nardini et al. 2008).
There are a number of rules for modelling opinion formation in complex networks. For example, considering discrete opinions, in the voter model, randomly selected agents exchange their opinions by that of one of their neighbours (Krapivsky & Redner 2003). The agents might influence their neighbouring agents to change their opinions based on their strength and the neighbours’ threshold (Leskovec et al. 2006). In the evolution of continuous opinions on a network, the opinions of two connected agents are updated if their difference is less than a threshold, i.e. the agents have evolving opinions (Deffuant et al. 2001; Amblard & Deffuant 2004; Lorenz 2007; Kurmyshev et al. 2011; Hegselmann & Krause 2002; Guo & Cai 2009).
where f is the desired cost (or objective) function to be optimized, N is the network size and N_{ i } is the set of neighbours of agent i. μ is the convergence (or influence) parameter, which often takes a value between 0 and 1. This parameter controls the speed of convergence in such a way that small values of μ corresponds to slow but smooth convergence, while the large values of μ corresponds to faster but wavy convergence.
To some extent, the above model for opinion formation imitates the behavior of agents in real social networks. A person may know many individuals in the society; however, he/she is only influenced by his/her closest friends (i.e., neighbors in the network). In many cases, individuals get the maximum influence through their best (closest) friends and try to make themselves similar to them, i.e., making their opinion closer to their closest friends. People try to behave like their best friends for establishing and maintaining their friendships and they influenced by them more than the others in their life. Sometimes these changes will happen because people want to preserve their connections and friendships and they will act or behave like their close friends (Barry & Wentzel 2006). They project their own attitudes and habits to their friends. Furthermore, research showed that, in general, the influence of the very best friend approximately is equal or comparable to the influence of multiple friends (Berndt & Murphy 2003).
where ϵ is a small value (in order to make the denominator nonzero). The above weighted update rules can be justified as follows. Let us suppose that an objective functions is to be minimized. As the bestmatching neighbor is found for each agent, it influences the agent according to its fitness, i.e., its value in the objective function. To this end, the weight for the update rule of an agent gets as the fitness function at that agent divided by the fitness value of the bestmatching neighbor, often resulting in a value in the range 0–1 (note that the opinions are updated only when the fitness of bestmatching neighbor is better than that of the agent). It is worth mentioning that in some cases, the opinions are in multi dimensions, i.e., x is vector, in which the best matching agent is obtained separately for each dimension.
The method largely depends on the diffusion of good opinions (i.e., those that are good in terms of the objective function) in the network. Agents with opinion values close to the optimal objective function disseminate their opinions through communicating with their neighbors, i.e., getting into consensus with them. Indeed, influence of opinions is in two folds. Indeed, closer opinions to the optimal value of the objective function have a better chance to be selected as bestmatching neighbors.
The above rule for opinion formation is somehow inspired by communication in human societies. Our friends influence our behavior in daily life; however, we are usually affected only when our friends are better than us. Here, similarly, for each agent, first, the best matching agent is found, and then, its opinion is updated (using equation (2)) if the fitness of the bestmatching neighbor is better (i.e., it results in a lower value in the objective function) than that of the agent.
It is worth mentioning that the consensus (or synchronization) properties of dynamical networks largely depend on their structure and some topologies are favored for fast consensus (Belykh et al. 2005; Ajdari Rad et al. 2008). Network topology plays also important role in the evolution of other dynamical phenomena over complex networks, such as evolution of cooperative behavior among interacting agents (Perc & Szolnoki 2010; Perc 2009).
A pseudocode of the proposed consensusbased optimization algorithm is illustrated in section PseudoCode as follows.
PseudoCode for the proposed consensusbased optimization method
Function CBO
N: number of agents in the population (network size)
M: number of attributes of opinion vector
Boundaries: the range of the opinions
F: desired objective function which is needed to be optimized (minimized in this case)
Begin

Initialize N * M matrix X by a random normal distribution for the opinion values in Boundaries;

net = Create a structured network;

Repeat

for each agent i in population do

for each attribute a do

neighbors_opinion = mask other attributes of the opinions x in neighbors of agent i in network net by a dummy value;

self_opinion = mask other attributes of the opinions x agent i;

j = find the best agent in neighbors_opinion resulting in the best value for F;

if neighbor_opinion of agent j optimizes F better than self_opinion then$\begin{array}{l}\mathit{\text{weight}}=\frac{F\left(\mathit{\text{neighbors}}\_{\mathit{\text{opinion}}}_{j}\right)+\u03f5}{F\left(\mathit{\text{self}}\_\mathit{\text{opinion}}\right)+\u03f5};\\ x\left[i,a\right]=x\left[i,a\right]+\mu \xb7\mathit{\text{weight}}\xb7\left(x\left[j,a\right]x\left[i,a\right]\right);\\ x\left[i,a\right]=\text{mod}\left(x\left[i,a\right],\mathit{\text{Boundaries}}\right);\end{array}$

end if

end for

end for

Until stopping condition(s) has/have been met

End
In the beginning of the process, the agents are initialized by some random values in a range acceptable by their opinion values. As indicated by Watts and Dodds (2007) “a minority of individuals who influence an exceptional number of their peers” (Watts & Dodds 2007), there is often a minority of agents that have a significant influence on others, which is mainly due to their specific position in the network The hypothesis of influential agents demonstrated that initiating influential individuals will be explicitly different from initiating noninfluential ones in the size and likelihood of a cascade (Watts & Dodds 2007). This means that initial opinions for influential agents probably would bias the result of the consensus. This phenomenon will not happen in proposed method, since CBO is not based on the bounded confidence model. Every agent selects his/her bestmatching neighbor regardless of its great social power and degree.
Consensus of opinion values
The above lemma was proved in (Seneta 1981); however, we also give another proof using a simpler method.
It is clear that in the above representation, matrix A is a stochastic matrix.
Theorem 1: The product of two stochastic matrixes is a stochastic matrix.
Therefore, C is a matrix with nonnegative entries and rowsums of equal to 1, and thus, it is a stochastic matrix.
Let t_{1} and t_{2} represent time steps (t_{1} < t_{2}) and B(t_{1},t_{2}) = A(t_{1}1)A(t_{1}2)A(t_{1}3)…A(t_{2}), which models the accumulated weights between time t_{1} and t_{2} (Hegselmann & Krause 2002). It can be simply shown that for any r ≥ 0, 1 – r ≤ e^{–r}.
Theorem 2 (Convergence Theorem): Considering opinion update rule (2), suppose we have a matrix B(t_{1},t_{2}) = [b_{ ij }(t_{1},t_{2})], which is a stochastic matrix and models accumulated weights where b_{ ij } is an element of matrix B the sequences 0 = t_{0} < t_{1} < t_{2} < … ≤ T and δ_{1}, δ_{2}, …, δ_{ i }, … are such that 0 ≤ δ_{ t } ≤ 1 and ${\sum}_{t=0}^{\infty}{\delta}_{t}=\infty}.$. If ${\sum}_{k=1}^{\infty}\mathit{min}\left\{{b}_{\mathit{\text{ik}}}\left({t}_{m},\mathit{tm}1\right),{b}_{\mathit{\text{jk}}}\left({t}_{m},{t}_{m1}\right)\right\}}\ge {\delta}_{m$ for all m ≥ 1 and 1 ≤ i, j ≤ N, then for any initial condition, there exists a consensus, i.e., lim _{t → ∞}x_{ i }(t) = x* for i = 1, …, N.
Proof: see the Appendix section.
Optimization tasks
We applied our optimization procedure on a number of benchmark problems and compared its performance with some wellknown methods including genetic algorithms (GA), particle swarm optimization (PSO), differential evolution (DE) and distributed dual averaging (DDA) algorithms. GA has been successfully applied to many optimization problems and was used as a basic paradigm in this work. It starts with a population of some random solutions – denoted by chromosomes. Therefore, the first step is to encode the initial solutions from phenotype to genotype. The objective function is then used for ranking the chromosomes. GA works iteratively and, in each step, uses some operators such as parent selection, recombination or cross over and mutation (Holland 1975). There are a number of parameters that should be tuned in order for a GA to work well. These include crossover probability, mutation probability, population model and parent selection models. Crossover probability P_{ c } indicates the probability of creating a new chromosome from two parents. Mutation probability P_{ m } indicates the portion of the population that undergoes mutation in each iteration of the algorithm.
DE is one of the bestperforming evolutionary algorithms frequently used for optimization tasks, which often results in the optimal solution in shorter steps as compared to other optimization algorithms. DE uses the difference of a randomly selected pair of chromosomes – indicating diversity of the population – and adds it to one of the chromosomes in the population. Then, it uses crossover operators such as binomial and exponential crossover to combine the chromosomes (Storn & Price 1997). The parameters of the algorithm are as follows. β is a real value showing the coefficient of the difference between two selected chromosomes and controls the amplification of differential variation. P_{ r } indicates the probability of using the mutant (trial) vector. N_{ v } is an integer number indicating the number of couple chromosomes in calculating the mutant vector.
We also compared our algorithm with PSO that is a wellknown optimization algorithm based on swarm intelligent (Kennedy & Eberhart 1995). In this algorithm, there is a population of agents called swarms (or particles) interacting with other agents – like our algorithm. PSO has two components: cognitive and social components. The cognitive component is the experience of each particle while the social component is the experience of the community the agents belong to. PSO has shown high degree of flexibility and acceptable speed in solving many optimization problems. Here we used one of the best extensions of PSO that is PSO with Inertia weights (Eberhart & Shi 2000). This feature plays an important role in balancing the powers of exploration and exploitation and making the algorithm more stable. PSO has a number of control parameters. Let us denote the parameters controlling the cognitive and social power of the algorithm as c_{1} and c_{2}, respectively.
Distributed dual averaging (DDA) algorithm – inspired by Nesterov’s dual averaging algorithm (Xiao 2010; Nesterov 2009) – has been proposed for optimizing convex functions (Duchi & Wainwright 2012). Similar to CBO optimization algorithm, DDA is a networkbased optimization method in which each node computes subdifferential of a local function while receiving information from its neighboring nodes. There is also a weight matrix to model the weighting process of the method. In any iteration, each node updates its solution vector by multiplying the stochastic weight matrix by the summation of its neighbors’ parameters and the subgradient of the objective function. DDA is computationally efficient and the convergence time depends on properties of the objective function and underlying network topology. Expander graphs have been proposed as efficient connection topology for DDA optimization algorithm (Duchi & Wainwright 2012). Alternating direction method of multipliers (ADMM) is another optimization method which uses properties of dual decomposition and augmented Lagrangian methods simultaneously (Boyd & Vandenberghe 2004). The Lagrange dual function is obtained by convex conjugate definition and the dual problem is solved using gradient ascent.
Benchmark problems
which is a function with many local optima. The optimal point for which the minimum 1 is achieved for this function is at x^{ * } = 0.1.
In all above functions except F_{1}, x ∈ [−5, 5]^{ D }. Furthermore, the global optimum  which is F_{2}^{*} = F_{3}^{*} = F_{4}^{*} = F_{5}^{*} = 0  is achieved at x^{ * } which is a random and different vector of real numbers in each run.
Network structures
One of the key ingredients of the proposed optimization algorithm is the graph structure used for connecting the agents, which is kept unchanged during the optimization process. In other words, the set of neighbours are not changed for the agents. In this work, we used a number of wellknown graph structures including, ErdősRényi random, WattsStrogatz smallworld and BarabasiAlbert scalefree networks.
We used the model introduced by Erdős and Rényi for construction of pure random networks (Erdős & Rényi 1960). In this model, N nodes are considered and each pair is connected with probability P. Research showed that real networks are neither random nor regular but somewhere in between; they are indeed smallworld. In order to construct smallworld networks, we used the original model proposed by Watts and Strogatz, as follows (Watts & Strogatz 1998). Starting with a regular ring graph in which each node is connected to its knearest neighbours, each edge is rewired with probability P, provided that selfloops and duplication of edges are prohibited. They showed that for some intermediate values of the rewiring probability P, we obtain a network with low characteristic path length, comparable to that of random networks, and high clustering coefficient (i.e., transitivity) that is much higher than corresponding random networks.
ErdősRényi and WattsStrogatz models result in networks with almost homogeneous degree distribution. However, it was shown that many real networks have heterogeneous degree distribution; there are many lowdegree nodes in the network, while a few nodes are hubs with high degrees (Albert & Barabasi 2002; Barabasi & Albert 1999; Barabási 2009). Barabasi and Albert proposed a preferential attachment growth model for constructing such networks, which is used in this work (Barabasi & Albert 1999). The model starts with a k + 1 alltoall connected nodes. In each step, a new node with k links is added to the network. This node tips to the old nodes with probability that is proportional to their degree, i.e., the higher is the degree of an old node in the network, the higher the probability of the making connection with the new node. The model results in scalefree networks whose degree distribution obeys a powerlaw (Barabasi & Albert 1999).
Results and discussion
All algorithms could find the optimum solution for objective function F_{3} as expressed by equation (11) with the results shown in Figure 5. However, CBO algorithms showed much faster convergence than others. In terms of network topology in CBO, BA and ER topologies worked better than the case when WS model used for constructing interaction topology. CBO was also better than GA, DE, PSO and DDA in optimizing the objective functions F_{4} as expressed by equation (12) and F_{5} as expressed by equation (13) and the results are shown in Figure 6 and 7, respectively. While, ER topology resulted in a bit faster convergence than BA and WS for F_{4}, their performance was almost the same for F_{5}. It is expected that consensus on random networks should be the fastest as compared to the one in BA and WS networks. This is mainly due to the fact that random networks often have shorter average path length compared to other models.
Iteration count averaged over 50 runs till convergence happened in optimization methods include distributed dual averaging (DDA), genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO) and the proposed consensusbased optimization (CBO)
DDA  GA  DE  PSO  CBO  

F_{1}  6  6  26  9  3 
F_{2}  63  44  535  26  12 
F_{3}  13  18  87  13  9 
F_{4}  134  49  365  21  13 
F_{5}  123  55  121  24  31 
In sum, our experiments showed that performing an optimization task with a simple consensus network provides the solution with a better performance than a number of classic optimization tools including GA, DE, PSO and DDA. Furthermore, we found pure random topologies constructed by ErdősRényi model more effective than smallworld topologies constructed by WattsStrogatz model and scalefree topologies obtained through BarabasiAlbert preferential attachment model.
Conclusion
In this paper we introduced a novel application for consensus phenomenon in complex networks. Consensus in networked structures has many applications ranging from engineering (e.g., sensor networks) to sociology (e.g., opinion formation in social networks). In this manuscript, we used network consensus to solve optimization tasks. We considered a number of agents interacting over a networked structure with topology as random, smallworld or scalefree. Furthermore, each agent was associated with an opinion value which could change in collaboration with neighboring agents. The agents worked collectively with their friends (which was defined based on the considered network topology and was kept unchanged during the optimization process) to solve an optimization task. To this end, each agent adapted its opinion value based on the bestmatching neighbor, i.e., the neighbor with the best value in the objective function. The proposed consensusbased optimization (CBO) method was applied on a number of benchmark problems and its performance was compared with that of a number of classic optimization tools such as genetic algorithms, differential evolution and particle swarm optimization. Our experiments showed that CBO could always find the optimal solution faster and more reliable. We also found ErdősRényi random topology better than WattsStrogatz smallworld and BarabasiAlbert scalefree topologies for which it could solve the optimization task faster when used in CBO as connection graph.
Appendix
And this completes the proof.
Declarations
Acknowledgment
The authors would like to thank Dr. Hamid Beigy for his insightful comments and discussion on the topic.
Authors’ Affiliations
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